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Guidelines for Artificial Intelligence in Academics at Ashland University

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Guidelines for Artificial Intelligence in Academics at Ashland University

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Guidelines for Artificial Intelligence in Academics at Ashland University

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Sample Syllabi Policies for AI Generative Tools

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Sample Syllabi Policies for AI Generative Tools

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Syllabi Policies for AI Generative Tools

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Keeping Up With State and Local AI Policies and Guidelines

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Keeping Up With State and Local AI Policies and Guidelines

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This guide is intended to help you stay abreast of governmental guidelines regarding AI. In addition to the information provided by the authors, there is a slew of helpful links included.

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Artificial intelligence will transform government operations, from back-office employee functions to the front door of constituent and customer experience. Some state and local governments are already seeing the benefits of AI, from improved contact center operations and greater automation to expanded accessibility features that respond intuitively to constituent needs.

With the advent of generative AI (GenAI), governments have even more opportunities to streamline operations and deliver personalized constituent experiences. GenAI can automate information retrieval, categorize insights and generate unique responses to user requests.

Some organizations are embracing this emerging technology for new uses and pilot programs. Others have adopted a more cautious "wait-and-see" approach. Regardless, it's imperative that governments develop guardrails and regulations to mitigate legal, ethical, privacy and financial risks.

Regulatory frameworks that address the use of AI and GenAI focus primarily on data protection, IT infrastructure security, responsible use of constituent data and trustworthy interactions between virtual assistants and the humans who use them. In the absence of federal AI statutes, some states, including Maine and Washington, have banned GenAI use until well into 2024. Others, like California, Massachusetts, New York, Oklahoma, Rhode Island and Pennsylvania, have established policies around how government entities should procure and use AI and specifically, GenAI. Additionally, some cities, including Boston and Seattle, have established their own policies for the use and regulation of GenAI.

The following is a consistently updated list of generative AI policies, guardrails and other guidance being implemented by states, cities and organizations throughout the country.

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Keeping Up With State and Local AI Policies and Guidelines

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Regulations around generative AI are rapidly evolving. This list will keep you up to date on what governments are doing to increase employee productivity and improve constituent services while minimizing risk.

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Member QuickTalk | Drafting an Institutional AI Policy

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Member QuickTalk | Drafting an Institutional AI Policy

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Academic Success Tip: Establish Guidelines for AI Use

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Academic Success Tip: Establish Guidelines for AI Use

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Inside Higher Ed | Higher Education News, Events and Jobs

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Higher education practitioners have been grappling with the threats and benefits of assistive artificial intelligence technology, like ChatGPT, for the past year, unsure of whether to shun or embrace the new tools.

A new report from Cornell University's Center for Teaching and Learning, "Generative Artificial Intelligence for Education and Pedagogy" suggests instructors adopt one of three policies-prohibit, allow with attribution or encourage generative AI (GAI) use.

Each decision takes into account academic integrity, accessibility, privacy and how the different frameworks can be relevant to different disciplines, according to the report.

The group produced three recommendations:

The Background

Cornell administration created a committee to establish guidelines and recommendations for GAI use at the university in spring 2023. The committee had three goals:

1. Evaluate the feasibility, benefits and limitations of using AI technologies in educational settings and how they impact learning.

2. Assess the ethical implications of using AI tech in the classroom.

3. Identify best practices for integrating AI tech into curriculum and methodologies, including recommended guidelines for safe and effective use and how to evaluate and improve AI use.

Rethink learning outcomes. For many students, their careers will be in GAI-enabled industries soon, which professors should consider in their teaching and learning outcomes. Leaders should prioritize higher-level objectives, critical thinking and skills for future careers.

Address safety and ethics. Current AI technologies have pitfalls, and professors should share those with students to approach GAI critically and validate the information AI produces.

Explicitly state policies for GAI. Expectations for use of GAI in assignments and classes should be clearly and consistently communicated to students. Examples of expectations include when it is allowed, what uses are considered violations of academic integrity, how the technology use should be attributed and how students should validate information. The group discouraged use of GAI for student assessment and the use of automatic detection algorithms for academic integrity violations (like Turnitin).

For higher education leaders looking to establish policies or for professors unsure of how they should address GAI in the classroom, the Cornell committee described three frameworks:

Prohibiting GAI: AI has the greatest potential for academic integrity violations when students use the tech to complete assignments meant to develop skills such as problem sets or research essays.

To avoid the risk of academic integrity violations, professors can move to assessment methods less suited to GAI tech, such as timed oral and written exams or in-class written essays. Professors can also modify assignments to be more specific to class content.

However, a move to in-person assessment and assignments should also take into account policies and practices for accommodating students with disabilities or other marginalized students, such as English language learners.

At present, few tools are capable of regulating AI usage in the classroom through AI detectors, because they rely on statistical analysis. Relying on these tools could result in unfair academic integrity violation accusations and create distrust between instructors and students, which would damage the learning environment, according to the report.

Professors should also emphasize why completing assignments without assistive AI is necessary to meet learning outcomes, why each learning outcome is important for academic and personal growth, and why integrity violations are harmful to the individual and the larger academic community.

Responsible use of GAI: Generative AI will likely have a place in many students' lives after graduation, so it is the responsibility of the institution to share ethical and productive uses of GAI when appropriate. The tools can also support students with disabilities, particularly those who experience difficulties with cognitive processing.

Some examples of responsible GAI in the classroom include:

Supporting writing skill development in planning, outlining, editing and individualized feedback. Guided use of GAI should be encouraged to decrease the potential of plagiarism and increase creativity and originality in writing.

Technical and mathematical courses may benefit from GAI use, including generating text to synthesize code for data analysis and visualizations. Sometimes GAI can "hallucinate," making problems inaccurate or circular in reasoning, so instructors should be aware of system limitations. Programming courses can adapt GAI for checking code, generating ancillary tools and creating explanations for code to text. Students may rely too heavily on the tool and not learn the skills necessary to generate working, understandable and updatable code, so GAI should be used as a tutor, not a sole creator of code.

Do you have an academic success tip that might help others encourage student success? Tell us about it.

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Report: Three ways to address generative AI in college classes

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As professors grapple with new technologies in the classroom, a report from Cornell offers suggestions for best practices and policies related to generative artificial intelligence. Higher education practitioners have been grappling with the threats and benefits of assistive artificial intelligence technology, like ChatGPT, for the past year, unsure of whether to shun or embrace the new tools.

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Keeping Up With State and Local AI Policies and Guidelines

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Keeping Up With State and Local AI Policies and Guidelines

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Artificial intelligence will transform government operations, from back-office employee functions to the front door of constituent and customer experience. Some state and local governments are already seeing the benefits of AI, from improved contact center operations and greater automation to expanded accessibility features that respond intuitively to constituent needs.

With the advent of generative AI (GenAI), governments have even more opportunities to streamline operations and deliver personalized constituent experiences. GenAI can automate information retrieval, categorize insights and generate unique responses to user requests.

Some organizations are embracing this emerging technology for new uses and pilot programs. Others have adopted a more cautious "wait-and-see" approach. Regardless, it's imperative that governments develop guardrails and regulations to mitigate legal, ethical, privacy and financial risks.

Regulatory frameworks that address the use of AI and GenAI focus primarily on data protection, IT infrastructure security, responsible use of constituent data and trustworthy interactions between virtual assistants and the humans who use them. In the absence of federal AI statutes, some states, including Maine and Washington, have banned GenAI use until well into 2024. Others, like California, Massachusetts, New York, Oklahoma, Rhode Island and Pennsylvania, have established policies around how government entities should procure and use AI and specifically, GenAI. Additionally, some cities, including Boston and Seattle, have established their own policies for the use and regulation of GenAI.

The following is a consistently updated list of generative AI policies, guardrails and other guidance being implemented by states, cities and organizations throughout the country.

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Keeping Up With State and Local AI Policies and Guidelines

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Regulations around generative AI are rapidly evolving. This list will keep you up to date on what governments are doing to increase employee productivity and improve constituent services while minimizing risk.

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Cross-Campus Approaches to Building a Generative AI Policy

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Cross-Campus Approaches to Building a Generative AI Policy

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Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effective policies can pay significant dividends.

Credit: vinnstock / Shutterstock.com © 2023

Higher education focuses on building knowledge and investing in the written word, whether it's scholarly research or demonstrations of learning such as essays, research papers, theses, or dissertations. The underlying assumption is that such work could not be significantly faked aside from paying another individual (such as student paper mills) or taking the work of others without attribution. Generative AI upends much of that baseline and in fact will challenge many institutions' academic integrity policies because they often prohibit copying from websites or acquiring papers from other individuals. Generative AI will also introduce other ethical and procedures considerations throughout the institution.

This piece evolved from our institutional roles (instructional design, writing support, and research and library support), our individual work at our respective institutions, and a half-day workshop on generative AI policy development that we facilitated for the NERCOMP 2023 Annual Conference. The workshop included a student panel, followed by focused conversations around several topics related to generative AI: ethics and plagiarism, bias, instructional technology, opportunities, and threats. For the final part of the workshop, we facilitated a mini design sprint for policy development.

The Need for Policy

One of the most pressing examples of areas for guidance is the discourse around plagiarism and how to detect it in an era of AI-generated content. Institutions are under pressure (whether organic or manufactured) to respond in some manner to the rise of generative AI as a widely available tool. The first wave of awareness seemed to be generated around plagiarism concerns and worker replacement theories ("Students will never write original papers again" and "Robots are replacing everyone"). For those in higher education, the drumbeat of fearmongering about plagiarism has succeeded in capturing faculty and administrative attention.

Early on, some assumed that generative AI work submitted by students could be identified with generative AI plagiarism checkers. TurnItIn's documentation states, "Our AI Writing detector's false positive rate is less than 1% for documents with 20% or more AI writing (our tests showed that in cases where we detect less than 20% of AI writing in a document, there is a higher incidence of false positives)." However, after the TurnItIn AI Detector was used in practice, across 38 million papers, false negative and positive detections are now at a higher risk. TurnItIn and other companies claim to offer greater odds of correct detection than reality seems to bear out. Additionally, further research is showing biases in what AI-plagiarism detection tools flag as being AI generated, including an increased likelihood of inaccurately flagging multi-language learners' work as having been generated by AI. Beyond the problem of false accusations, this environment also creates an untenable situation for students who must somehow defend themselves against a machine that cannot show its work but is just a projection. Additionally, students are using tools such as QuillBot, which will paraphrase text and use synonyms, to circumvent AI detection tools.

As original student writing becomes increasingly difficult to verify and confirm, developing a policy for AI detection might result in its being outdated just as the policy is implemented. Having an agile committee representative of diverse campus needs who can both review these issues and considerations to craft a more useful policy is part of what is needed to both protect students and choose the right tools for navigating the technological changes.

This is but one issue that higher education needs to navigate when crafting policy around generative AI. Other issues include but are not limited to the following:

The role of generative AI in visual and textual outputs of the institution, be they for marketing, for social media, or in reports

How and where faculty can use generative AI in the creation of course content, assignments, and feedback or assessments

Addressing the embedded biases of the data and outputs

The impact and challenge of information literacy

The environmental impact of generative AI, including greenhouse gasses and water usage

The impact on workers needed to run generative AI

Starting with the End

In many areas of life, including instructional design, an ideal place to start is with the results-what you want the outcome to be or do. To feel that your-and your collaborators'-time has not been wasted, and to see that you have made progress toward what might still be an abstract version of achievement, codify what success looks like for you, your collaborators, your stakeholders, and the final institutional arbiters. As AI continues to develop at a rapid pace, it is important to pause and do enough research so that you can formulate the questions you ultimately seek to answer. Below are some questions to get you started in thinking about this policy:

Whom is the policy going to focus on? Students, faculty, staff, administration, third-party vendors, contractors, etc.?

Can the same policy apply across the institution, or will different policies be needed for different parts of the organization?

Will the policy stand on its own, or will there be room for adjustments (for example, will students encounter variations depending on whether instructors-under the notion of academic freedom-want to encourage or discourage certain uses of generative AI for the purposes of teaching)?

What can or will be the implications of violating the policy?

What methods of accountability with the policy can be created when it may be hard to actually detect generative AI text?

Will there be differences between institutionally affiliated generative tools and those that are available to anyone?

Perhaps you are still in the fact-finding and idea-generating stage-you might need to specifically dedicate time to consider what will constitute your goal for the mission. You may decide that success for your group means developing and writing down these measurable goals. That is, you might still be in the phase of brainstorming and research, and you might need to take time to decide what your goals are. Ultimately, all involved should agree on specific measurable outcomes that are necessary for your group to have completed your work.

It is natural to feel a sense of urgency to take action, and it might be tempting to rush through this step. Although this process will not necessarily be easy, it will be helpful in carving out exactly what you are going to do in this phase. One of the trickiest parts about generative AI is that it has continued to change and shift in the past 12 months and will likely continue to do so, especially as other forms of generative AI (image, audio, video, slides, etc.) become increasingly available. Therefore, no matter what your goals are, creating a mechanism for revisiting, adjusting, updating will be equally important to name and anticipate early on. In that case, think in terms of not getting it perfect the first time through but having a process to iterate.

Identifying Stakeholders

Generative AI has the possibility of hitting every part of the institution. Because it is difficult to imagine an area that generative AI won't touch, it's useful to think about all the institutional stakeholders who will need guidance around usage of these new tools. Start by considering all the user groups and their iterations at your institution. One approach would be to conduct an audit of your campus; if your institution has an organization chart, that is a great document to use as the foundation. Some people and groups are likely not represented, so detective work will come into play. This is a great opportunity to break down institutional silos and examine your own invisible biases about the roles others play on our campuses.

Identify non-classroom student- and faculty-facing roles, which could include instructional designers, writing specialists, librarians, and academic support services. Then move on to people whose work has an impact on the campus experience as a whole. This could include staff working in IT (e.g., information security, academic computing, or web services), institutional communications, student life, even alumni relations. Be mindful that just because a group is not readily visible to you does not mean that they will not be impacted by or have an impact on your policies. We are a good example of coming together to collaborate on AI policy. The diversity of our professional perspectives has lent itself to not only seeing different viewpoints but also creating the opportunity to truly understand the nuances in just how many ways AI is impacting our institutions.

Consider, too, your contingent communities, such as part-time educators (adjuncts) and part-time staff. How are they included in the community in general? Do they experience regular communication patterns as the full-time employees do, such as being included in campus-wide emails? Will you need to set aside time to engage them in discussion separately, and will they need more training and professional development as you enact the policies you create? Look for allies who can serve as conduits to get people involved. For instance, adjuncts are often on campus for limited amounts of time during the day or might work elsewhere during typical business hours. When you are scheduling conversations, be mindful that you will need to create opportunities for those who work nontraditional schedules to be involved.

Some questions to consider for other entities at your institution may include the following:

Will the institution's upper management consider using generative AI to surveil employees' work to detect efficiency or generate employee evaluations?

Should your policymaking efforts account for this scale of institutional use?

How will AI be addressed in human resources, especially the recruiting and hiring process?

In what ways will the community outside the campus be impacted by your policies?

Do you have community partnerships in which you offer students an opportunity to put their theoretical learning into practice? Do you have an obligation to educate your students on the uses of generative AI as part of that collaboration?

Will your institution be viewed as being a policy and position leader on the subject of generative AI?

What will be reasonable and equitable means of challenging outputs by generative AI?

Finally, one of the most visible and yet frequently overlooked communities is the students. The value of engaging students in determining this policy cannot be overstated. To craft a policy without their input could result in a policy that feels out of touch and irrelevant to them. At the NERCOMP workshop, the highlight was hearing at the student panel how they demonstrated their own deep and sophisticated thoughts about generative AI and its roles in their lives. In particular, the conversation should extend beyond plagiarism and also include student perspectives about how other institutional areas should be using it (e.g., faculty, marketing, communications). Many students are aware of and interested in how generative AI is currently being employed in a wide variety of industries. Students might be reading about the way Paul McCartney used generative AI to restore John Lennon's voice to create a "new" Beatles track, or they may be interested in how generative AI is being used to dramatically improve recycling programs. These interests might be general curiosity (to be encouraged!), but they might also tie into students' future employment goals.

The consequences of not including a diverse array of groups in these conversations far outweigh any inconvenience you may experience when trying to find times and ways to gather input throughout the policy development process. Our workshop included people from many of the groups mentioned, and a key takeaway was just how much we learned from each other. Many realized that while something may seem obviously important to you, it may not even be considered by someone working in a different position.

Models for Developing Policies

Depending how an institution is structured and how much its leaders want to include different groupings within the organization, any of several models can be deployed to develop policies around generative AI. The following options are useful approaches and, to some degree, can be mixed and matched to meet the institution's needs and structures.

Task Force Model

Put out a call to action to form an inclusive team representing all aspects of the institution to create a policy task force. Alternatively, it could be a smaller, nimbler team that works to engage with different stakeholders across the institution, creating a template policy for generative AI and adjusting and tweaking it for each area of the institution, in conversation and collaboration with relevant stakeholders.

For instance, the policy for students and faculty is likely to look different from that of the marketing department, but realistically all of these groups should have AI policies. This might seem self-evident for students, but there are important questions to consider even for marketing, such as the use of generative AI image tools to represent students. Would that adhere to the institution's ethos or marketing ethics? Those are the kind of questions that would be relevant to marketing that students and faculty may not need to be involved in.

Governance Model

Some institutions have governance models in which faculty and staff play pivotal roles in the development and creation of institutional policy, particularly around policies that directly impact the classroom and students. This can be a useful model to gather a range of voices throughout the organization and have a clear pathway and record (e.g., committee notes) of how decisions are made and implemented. Yet these processes may encounter challenges in moving at an effective pace to make decisions that are timely and responsive to a changing environment. Due to the rate of change in generative AI, committees might create policies for assumptions around generative AI that are no longer valid.

Design Sprint Model

1. Understand: The first step is to understand how generative AI is currently being used (or isn't) at your institution, the concerns of the community, and the ideal state for AI use. This can be achieved through a listening tour, lighting talks, surveys, or structured conversations.

2. Define: Next, the policy team should review all the information gathered in the first step to define their main goals and desired outcomes that reflect the needs of the community.

3. Sketch: Each individual on the policy committee should sketch out their own draft policy that meets the definitions outlined previously. The larger committee can then review all policy drafts and then narrow down the ideas of each draft into a finalized Solution Sketch.

4. Decide: The policy committee will then review all Solution Sketches to decide which version they want to use to move forward with the process.

5. Prototype: The prototype can be considered the first draft of the policy to be shared with the community.

6. Validate: Return to the community members from the first phase of the design sprint process, gather feedback based on your policy draft, and make relevant edits. This is the last stage of the design sprint process, which will involve crafting the final version of the policy ready to be shared with your institution.

Consultant Model

Exemplar Model

Another option is to follow the lead of institutions that are publishing their own policies. For instance, Lance Eaton and his students have proposed and posted policies for College Unbound. Starting with one of these policies, the institution can then review, share for input, and adapt as needed. This can save a lot of time in terms of coming up with the initial approach, but it will need to be tailored to the specific needs of the institution and its population. If the policy creation team is reluctant to borrow from another institution, they could use a generative AI tool to draft the initial policy and workshop it across the institution. We used ChatGPT to generate several examples of policies to help get you started crafting your own.

Recommendations for Gathering Voices

To create the clearest and most effective policy, all stakeholders' voices must be heard. Because recruiting volunteers for their insights in drafting a policy can sound tedious, consider using both formal and informal routes to get their assistance. Also important is conveying how each stakeholder may be impacted by such a policy and emphasizing that their experiences and observations are highly valuable to the process. Asking for help drafting a policy document can be overwhelming to many. These approaches might make this more engaging:

Connect with people in mini roundtable discussions or small-group conversations

Find help through social media-use polling features to generate feedback and locate allies

Have one-on-one conversations with the people you see as your greatest supporters and greatest challengers in this process. Diverse perspectives are necessary to have a policy that works for everyone.

Allow for enough time in discussions. It can be easy to rush through things, but these discussions will include deep topics and issues that need time to be fully processed.

Provide collaborative documents for participants to add text, and use the comment feature for questions or additional thoughts.

Institutional knowledge is both an advantage and a disadvantage in this process. Some stakeholders will be obvious, and some people will quickly volunteer. Others may be more challenging to reach because of their disinterest or because they don't see the relevance to their work. These are important people to bring into the conversation, rather than making assumptions about their thoughts or possible contributions.

Framework for Generative AI Policy Creation

Below are the relevant sections that a generative AI policy should address to help institutions in their policy development processes. Each institution should have its own individual policy based on the needs of its community.

Policy Audience: Whom is this policy for? Is it for the entire institution, faculty, students, staff, departments, third-party vendors, or others?

Policy Timeline: What is the timeline for implementation? Should that timeline include a review and update cycle after initial implementation?

Policy Tools: What counts as AI for this policy? Is it focused on all AI or only generative AI? Is it focused on all generative AI or just text-generating AI?

Academic Integrity Guidelines: If the policy is related to student or faculty work, what are the integrity requirements to make sure academic integrity is upheld?

Acceptable Use: If AI usage is acceptable, are there any limitations on the amount of usage (e.g., a certain percentage must be individually generated in certain contexts) or purposes for which generative AI may be used?

Transparency: What practices are in place for communicating its usage throughout the institution?

Security and Legal Considerations: What concerns need to be addressed concerning privacy, intellectual property, and proprietary knowledge around using external or enterprise generative AI tools? Does endorsing the use of generative AI conflict with any laws such as the General Data Protection Regulation?

Ethical Considerations: What concerns or responsibilities does the institution have explicitly or implicitly within its mission that conflict with the environmental, human-exploitation, and bias issues related to generative AI?

Institutional Resources: Which areas of the institution will be committed to supporting, responding to, and implementing uses of generative AI?

Processes for Policy Violation: If the policy is violated, what are the steps for identifying and addressing it?

Conclusion

Institutional policy development can often be a methodical process that both informs and better unifies an institution's approach to a particular challenge. And yes, it can be tedious. Yet many institutions are often unprepared for technological developments, and the delays in catching up have a deep impact on students, faculty, and staff (e.g., the reactive response to the pandemic). Although the generative AI cat is out of the bag, there's still an opportunity to meaningfully guide-through a collaborative effort-how it can be best used by all the stakeholders. Such an approach will result in a better learning and working environment for students, faculty, and staff. Taking an iterative approach to AI policy might feel unnatural for those who operate best with exhaustive policy documents, but it just might be the key to successfully navigating this new technological reality.

Notes

1. Daniel E. O'Leary, "Gartner's Hype Cycle and Information System Research Issues," International Journal of Accounting Information Systems 9, no. 4 (December 1, 2008): 240-52; Jackie Wiles, "What's New in Artificial Intelligence from the 2022 Gartner Hype Cycle," Gartner, September 15, 2022.

2. Sam Levine, "Vanderbilt Apologizes for Using ChatGPT in Email on Michigan Shooting," The Guardian, February 22, 2023; Josh Moody, "The ChatGPT Commencement Address," Inside Higher Ed, June 29, 2023; Lauren Coffey, "Harvard Taps AI to Help Teach Computer Science Course," Inside Higher Ed, June 30, 2023.

Esther Brandon is Manager of Learning Design & Technology Adoption at Harvard Medical School.

Lance Eaton is Director of Faculty Development & Innovation at College Unbound.

Dana Gavin is Director of the Writing Center at Dutchess Community College.

Allison Papini is Assistant Director/Manager of Research & Instruction Services at Bryant University.

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cross-campus approaches to building a generative ai policy

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Cross-Campus Approaches to Building a Generative AI Policy

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Particularly for new technologies that disrupt long-standing practices and cultural beliefs, the work of carefully and intentionally developing effect

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This study from Educause is provided to "summarize the higher education community's current sentiments and experiences related to strategic planning and readiness, policies and procedures, workforce, and the future of AI in higher education." Briefly, it should give you a sense of the higher education landscape and AI's evolving role.

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Moving from reaction to action, higher education stakeholders are currently exploring the opportunities afforded by AI for teaching, learning, and wor

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CITE has developed an online asynchronous training for Ashland University faculty on AI Literacy. It consists of 3 modules:

1. The Context & Significance of Generative AI

2. Ethics & Academic Integrity

3. Practical Guidance for Teaching with AI

To access this training, please navigate to the CITE Blackboard Org, click to expand the AI & Academic Integrity Resources learning module, and select AI Literacy Training.

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Inside Higher Ed | Higher Education News, Events and Jobs

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I wrote a dissertation on a topic so untrammeled, Catholic women's higher education, that there were no books from which to plagiarize. Later, I jumped off a tenure track at the University at Buffalo to go to law school, meaning I did not embark on a scholarship path to put me into the line of fire that has recently emerged around this topic.

In my administrative days, I wound up writing a lot, including this blog. But the form was freer; it was not scholarship but commentary on contemporary law, policy and politics of the internet. With some exceptions I did not have to follow an academic style book. Hyperlinks became the new footnotes. Maybe the most truthful thing I have to say about my own work and the subject of plagiarism is that I am probably a little too bullish on my own ideas to need anyone else's.

I watch with concern, however, how the issue of plagiarism has reared its head in the general political attack on elites and higher education in particular. I would not be surprised to find that the publish-or-perish cycles of upward academic mobility have engendered a kind of sloppiness that is real among some of our most cherished scholars. When Doris Kearns Goodwin was found to have done the dirty deed, I was disappointed. I was even more dismayed when she explained that it was her researcher's original mistake. She has a researcher? Naïve me.

No doubt some who call out higher education have a political agenda. Need I mention the names Christopher Rufo and Elise Stefanik? That said, their expression puzzles me a bit. I confess when I applied for a position at an esteemed university in the last few years, I rolled my eyes at having to write a DEI statement. Part of the roll reflected a weary sense at this age and stage of my life and career lived around these issues before they even became a thing.

Has the pendulum swung too far to correct the wrongs of yesteryear? No, I don't think that it has. We still live with the consequence of so many -isms. But I do think that in some instances higher education has fallen into certain rigidities and even silliness about how to approach these important issues. Calling those excesses out is OK, but the vehemence, the anger, the vitriolic antipathy, a fundamental lack of respect for education? I don't get it. Apart from blind ambition, I do not understand why they take a jackass-knocking-down-the-barn approach to such a critical component of American greatness.

Now we have plagiarism as the vehicle for their attacks. Hoisted on our own petard, I suppose one might say. If a course of academic hygiene is called for, that project would not be a bad thing. The contemporary question is to ask what AI will bring to it. I suspect confusion. We already have copyright law newly in a snit about both the training of large language models and their output. Plagiarism is not the same as copyright, but it is an intellectual cousin. A year ago, I wrote in this blog about the academic integrity policy I now use in my courses to address consumer AI, i.e., ChatGPT. I can, however, foresee a blurring of the lines in academic work. Heck, if a noted scholar at MIT can take definitions from Wikipedia in a doctoral thesis, who might not be tempted to throw in a phrase or two from ChatGPT? The problem lies in the fact that ChatGPT does not always produce the same answer to questions, so detection might be a bit tricky. Turnitin, moreover, is irrelevant.

I devoted my career to higher education because I was so grateful to go to college. I wanted to do some form of service as my life work. I am by nature curious. I jumped off the rails in 2017 to run for Congress, and, as I have expressed in these posts many times, I learned a great deal from that experience. I now bring those lessons back to the classroom, where, even after years of administrative work, I am the happiest.

All of which brings me to these conclusions: we-humanity-will deal with AI as we have with so many other challenges historically. That is, imperfectly. Future historians will look back on this age in full view of our gifts as well as our faults.

Plagiarism is worthy of our attention. We-higher education-would do well to welcome the opportunity to take responsibility for our mistakes and clean up scholarship. We owe it to our students, to ourselves and to the generations of scholars who will succeed us. We might also learn a thing or two about the epistemology of the craft, especially as it distills into different disciplines. In keeping with our own spirit, this is a chance not merely to fix a problem, but to learn from it.

The most important lesson, however, is the political one. The attacks on higher education are serious, just as is our political landscape today. We must stand strong. We cannot allow this issue or any other (anti-"wokeism," or anti-intellectualism by any other name) to undermine our service, purpose or missions. No ivy tower, literal or figurative, will insulate us, nor should we seek insulation. Like journalism or even democracy itself, higher education matters at the most fundamental level to the quality of a culture and society that values free inquiry and speech, innovation and the pursuit of science and the arts for its own sake. What AI and the issue of plagiarism might offer us at this contentious time are tools and reason to reinvigorate the meaning of our institutions.

A final note: if higher education is looking for volunteers to engage opponents, sign me up. I would love nothing more than to apply what I learned in the groves of academe, as well as in the foxholes of electoral politics, to fight this fight.

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Seize the opportunity. I wrote a dissertation on a topic so untrammeled, Catholic women's higher education, that there were no books from which to plagiarize. Later, I jumped off a tenure track at the University at Buffalo to go to law school, meaning I did not embark on a scholarship path to put me into the line of fire that has recently emerged around this topic.

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Creating a Culture Around AI: Thoughts and Decision-Making

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Creating a Culture Around AI: Thoughts and Decision-Making

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Creating a Culture Around AI: Thoughts and Decision-Making

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Given the potential ramifications of artificial intelligence (AI) diffusion on matters of diversity, equity, inclusion, and accessibility, now is the

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Elon University's Human Wisdom Toolkit for the Age of AI

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Elon University's Human Wisdom Toolkit for the Age of AI

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Here, you can read the field guide produced by Elon University in partnership with the Princeton Review and the AAC&U, access a Teaching Guide composed of materials for 10 teaching modules, and explore the intellectual framework behind the project.

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Student Guide to Artificial Intelligence

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Promote Human Wisdom on Your Campus - Student Guide to Artificial Intelligence

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All materials are provided free and licensed under Creative Commons so educators can use and adapt the work for noncommercial applications. The Student Guide to Artificial Intelligence Series Elon University’s Imagining the Digital Future Center and the American Association of Colleges and…

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Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis

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Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis

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Per AI for Education: "This new resource summarizes what the evidence says about AI detectors and provides practical, research-informed guidance for responding to potential AI misuse. It also highlights proactive strategies that can help schools strengthen assessment practices and build AI literacy."

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Building Our AI Capacity: A Playlist for Educators

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Here, four free AI courses are recommended! These are general introductions to AI, rather than training specific to professionals in higher education.

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Here's the honest truth:

If you want to level up your career with artificial intelligence, you don't just need a list of AI classes and courses...

You need a list of the right AI classes and courses.

Why?

Because AI is moving fast. We've seen an explosion of disruptive AI innovation that could severely impact millions of knowledge workers across every single field in the next 1-2 years. You need to move now to level up your AI literacy. And you likely don't have time to waste on the wrong type of AI classes and courses.

What do we mean by the "wrong" type of AI classes and courses?

We have nothing against all the credible and competent educators out there. But the simple fact is: If you're just looking to level up your work fast, you don't need a PhD-level technical primer on deep learning, what a neural network is, or natural language processing. All you need to do is understand the fundamentals of AI and how these fundamentals directly apply to your work in order to thrive in the AI-powered economy.

(So, if you're looking for advice on full college classes or degrees to pursue, this isn't the post for you.)

But the vast majority of knowledge workers out there simply need fast, practical guidance on AI fundamentals and how to best use AI tools in their work.

The good news? You don't need to be a data scientist or machine learning engineer to do this. In fact, you can get further than 99% of your peers using the courses and classes we've collected here.

They're some of the best AI courses we've found since our company started educating business leaders on AI way back in 2016. The list is intentionally short, so you can focus on actually completing and acting on the AI education that'll move the need most for you. We also intentionally focused on free ways to learn AI, so you can get started no matter your budget.

Let's dive in.

The Best AI Courses Available for Free

1. Intro to AI from Marketing AI Institute

Intro to AI for Marketers is a free live online AI class offered by Marketing AI Institute. While it has the word "Marketing" in the name, it's applicable to any type of knowledge worker because in just 30 minutes, it teaches you exactly how to understand and get started with artificial intelligence, including:

What AI is, and why it matters

How to identify AI use cases in your work and business

How to find and evaluate AI technology vendors

What business outcomes AI can help you achieve

How to measure the value of AI tools on your company's efficiency and performance

How to prepare your team for piloting and scaling artificial intelligence

We created this class because it's the one we wished had existed when we got started.
Click here to register for the next Intro to AI class.
2. AI for Everyone from Andrew Ng and Coursera

In this course, Andrew Ng, one of the top minds in AI, introduces you to the fundamentals of artificial intelligence for business. The course is designed for beginners, and covers:

Core AI terminology, including neural networks, machine learning, deep learning, and data science

An overview of AI's realistic capabilities

How to spot opportunities to apply AI in your company

How to navigate ethical and societal discussions about AI

Click here to enroll for free.
The course takes about 6 hours to complete.

3. Introduction to Generative AI from Google

Google has an excellent introductory course specfically on generative AI that reveals how the technology works without getting overly technical. The course covers:

What generative AI is and how it works

An introduction to the large language models (LLMs) that power many generative AI applications

A primer on responsible AI and how Google applies responsible AI principles

The course has 5 total modules totaling about 5 hours of content.

Click here to take the course.

4. Introduction to Machine Learning from Google

This is a short 20-minute course offered by Google on the fundamental technology that powers many of the AI tools we use today: machine learning.

In it, you'll learn how to:

Understand the different types of machine learning

Understand the key concepts of supervised machine learning

Think about solving problems with machine learning vs. traditional approaches

Free AI Classes for Extra Credit

The classes and courses above will put you a mile ahead of the pack. But if you want to go deeper-and get much more technical-these more advanced (but still free) artificial intelligence and machine learning courses are also worth checking out.

Intro to Artificial Intelligence from Udacity

In a , AI experts Peter Norvig and Sebastian Thrun talked about their success teaching an online AI course with over 100,000 students. That course is available through Udacity, and provides a comprehensive overview of everything from machine learning to natural language processing to computer vision to robotics.

Be aware: Despite having "Intro" in the name, the course does include practical problem sets to complete and is listed as an "Intermediate" difficulty level, though no prior experience is required.

Machine Learning Specialization from Coursera and Stanford University

In another Coursera offering, Andrew Ng offers an in-depth machine learning course in partnership with Stanford University. In it, you'll learn how to:

Build machine learning models

Build and train a neural network

Apply best practices in machine learning development

Build recommender systems

This one is a doozy. It requires about 10 hours a week for 2 months, and requires having (or learning) some technical chops.

You can enroll starting for free by clicking here.

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The 4 Best Free AI Classes and Courses (2024)

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Want to level up your career with AI? You need a list of the RIGHT AI classes and courses. And we've got you covered.

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José Antonio Bowen is the author of the groundbreaking new book, Teaching with AI. Check out his brief introduction to generative AI.

P.S. Interested in the book? CITE is hosting a book club this spring. Reach out to Katy Major if you're interested.

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For a pdf of all slides and citations: click here

THE AI ECOSYSTEM: Click to open AIs and AI tools in multiple tabs in your browser.

PROPRIETARY FRONTIER MODELS: These are different AI models. They are from different companies, using different different neural networks and with different personalities and abilities. The paid versions are often substantially better and smarter.

THE BIG THREE

Claude.ai Lots of tokens for writing context and designed with a constitution, to do no harm. Sonnet 3.5 is GREAT and currently FREE. There is also a smaller Haiku and a larger Opus. Claude (in workbench mode) also now allows you to control the "temperature" from most random to most predictable.

ChatGPTIf you have only used the free version before May 14, you have been using GPT 3.5: forget everything you thought you knew. For an adult you need ChatGPT Plus (click on Upgrade to Plus) or the new ChatGPT 4o (omni) which is faster but similar to 4.0. You need to see 4o in the upper left (under the ChatGPT menu OR see 4o in the address.

(formerly Google Bard). For an adult you need Gemini Advanced (or Pro 1.5-Free for 2 months)

THE OTHER PROPRIETARY MODELS

Copilot Microsoft owns half of OpenAI so CoPilot is really another version of ChatGPT, but connected to the internet and now integrated into Microsoft projects. If your organization gives you access to this in MS Office you also are FERPA and HIPPA secure. If you click the "Creative" button in CoPilot, you might GPT 4 or Turbo. During peak times it often still only GPT 3.5. the Paid Pro gives you "priority" to 4 or Turbo)

Pi is focused on dialogue and role-playing.

WolframAlpha combines the computational powers of Wolfram|Alpha with ChatGPT.

Latimer (named after African-American engineer Lewis Latimer) aims to better represent diverse communities by adding further training from (verified and licensed) books, oral histories and sources from Black and Brown communities. (Latimer is a fine-tuned version of LLAMA.)

Poe is a consolidator and provides access to multiple AI through one interface.

Grok is available only to X subscribers.

OPEN SOURCE MODELS: There are now open source models that are almost as good as the best proprietary frontier models, and even better in some specialized areas. You can download most of these models from Azure (Microsoft) or HuggingFace. You can then fine-tune and run them on your laptop, which deals with most privacy issues but also transfers the security risk to you.

Meta AI is the chat version of Llama3. It does not require a login.

Huggingface is a chatbot running on Llama. Start here to get a sense of what open source can do. No login is required.

Llama 3 from Meta, with 70B parameters is very close in performance to the best paid models.

Qwen2-Math scores high (maybe the highest for pure LLMs?) at math.

DeepSeek-V2 is the hot new Chinese model that scores highly for coding. It is a large (238B parameters) but fast and efficient through the use of Multi-head Latent Attention (MLA) that combines even more values into tokens, but read the paper to understand.

Mistral 7B is an open source LLM from France that has 7.3B parameters.

APIs = APPLICATION PROGRAM INTERFACE for RESEARCH: This is a huge category and most of the new products you see are here using an API to interact with one of the frontier models for you. You can often replicate the results with longer and careful prompting, but these are very useful shortcuts. Here, for example, are some research tools:

Perplexity.ai is an AI-powered chatbot search engine. It answers your questions with the sources cited using multiple frontier models.

Consensus.app is an academic research tool that limits its data search to the 200M published papers in Semantic Scholar and uses AI (ChatGPT) claims, methodology, sample size and more, like a "consensus meter" that provides an estimate of the consensus in the published literature. Here is the result when asking "do brain games work?"

elicit.com and researchrabbit work in a similar ways. Researchrabbit is more like Spotify for research papers (you liked that, you should know about this) while Elicit might be faster for finding terms.

NotebookLM is Googles version of a research assistant but it works only on the documents you upload (up to 500,000 words EACH).

SciSpace also has similar functions but with a broader suite of tools, like a paraphraser that rewrites or helps explain passages (something you can also find in ExplainPaper.) All four of these are essential lit review tools.

Scite extracts citations and uses AI to analyze if they are cited with support or contradiction in other papers. Upload a pdf or your citations and find out quickly about the impact of the work.

Writing tools that use ChatGPT include Grammarly, Copy.ai and many many others.

Each of the big platforms also has a way to build and then distribute your own fine-tuned applications: GPTs (from OpenAI), Assistants (from HuggingFace), Bots (from Poe). Faculty developed writing tutors, for example, include one from Mark Marino, AI Tutor Pro from a group of Canadian faculty and MyEssayFeedback in beta from Eric Kean.

APIs for WRITING and MORE

WRITING: Grammarly, and Quillbot are already known to students (before they were enhanced with AI) and (along with the newer Caktus) blur the line of improving with cheating, offering to write paragraphs, solve problems, answer questions and check if your content can bypass AI detectors. Copy.ai and Jasper and many many others focus on specific types of writing or business uses.

NOTE-TAKING: Microsoft OneNote, Otter.ai, Fireflies, and Zoom Companion all do more than just transcribe notes, organize and summarize (often across multiple documents/meeting ). They can analyze who is talking (or interrupting) the most and some even the emotions of participants. Find the latest list of "best" on your platform.

STUDY ASSISTANTS: Both Nurovant and Turbolearn.ai can turn lecture notes, a pdf (or a phone recording) into an outline, practice tests, flashcards, quizzes or notes.

FINE-TUNED BOTS: Each of the big platforms also has a way to build and then distribute your own fine-tuned applications: GPTs (from OpenAI), Assistants (from HuggingFace), Bots (from Poe). Faculty developed writing tutors, for example, include one from Mark Marino, AI Tutor Pro from a group of Canadian faculty and MyEssayFeedback in beta from Eric Kean.

VOICE AND DIALOGUE: This exploded in May 2024).

Pi is focused on dialogue and role-playing-click the speaker icon in the upper right to have a conversation.

Hume.ai is in beta but claims to be even more emotionally intelligent than Pi.

GPT 4o is the real game-changer. Watch some of the videos to get a sense of how seeing and hearing (and interrupting) might be leveraged as a tutor or for translation.

IMAGE, SLIDE and VIDEO GENERATORS
MINI MODELS and EDGE AI: These are smaller, faster and more specialized (often) OPEN SOURCE tools that you customize to live and run on your phone. Note that the ways to make an LLM better are model size (see Frontier models above), data set size and and the amount of training. Since it is not clear that larger more capable models will be cost effective, these faster smaller models (with more training) may end up being more useful. Apple Intelligence will test this idea.

Phi-3 from Microsoft comes in three sizes Mini, Small and Medium (3.8-14B parameters)

OpenELM is the Apple version that comes in four sizes (270M-3B parameters)

Gemma is the open source smaller model from Google also in several sizes

BROWSER EXTENSIONS: One way to become more familiar with how AI works is to add an AI extension to your browser. If you use Chrome, some good free extensions are Perplexity AI, SciSpace (which does everything SCiSpace above does, but in your browser), Merlin AI, Bing, or Clipy AI: now every time you do a Google search, you will also get an AI response.

AGENTS: Devin is not quite there, but it gives a taste of what is to come. It is not a chatbot, but rather an "agent" that can plan and execute a series of tasks, like building you a website. Another use of agents (that is also about growing use of synthetic data) is this simulated hospital with AI agents as both patients and doctors, which allowed the Ai doctors to gain experience (treating 10,000 patients) and "evolve" become better.

THE PROMPTS: Copy, Paste & Customize
AI is Changing Work and Thinking

What might be unclear/missing/controversial in this email to my team/boss at company X? Make this email sound kinder and more inclusive.

I am hoping to convince my boss/company to support this idea. Read these emails/strategic goals and advise me how to align this proposal/request with my company or boss's values/language? Can you find a quote from my boss's favorite author that aligns with this project?

Identify what are the most important findings and insights in this report/link/article for me [position and place]. Specifically highlight anything that relates to topic X and note recommendations for what someone in my position should consider doing now/over the next year. Organize this into a 300-word report with bullet points and provide quotations and evidence from the report for each.

Suggest ten ways to make this assignment/project more motivating, engaging/relevant to engineers/basketball fans/my project manager/my team.

Create a quick game/icebreaker/activity for my team to introduce topic X.

Put this into simpler terms for new customers. Or create a relevant analogy that will explain how this works to a younger/older audience.

Watch/read this ad/message/email and tell me what audience X is most likely to think it means.

You are an innovative thinker with a very broad range of new ideas and experience. Provide 10 ways I might solve this problem. [Describe the problem in as much detail as possible]

Try more prompts here: https://docs.anthropic.com/claude/prompt-library or here: https://www.moreusefulthings.com/prompts

Analyzing Patterns

Analyze customer/employee feedback and find key concerns.

Review my email with employee X and identify common themes. Categorize the issues into groups.

Perform a sensitivity analysis of key assumptions in this data/proposal. RUn a Monte Carlo simulation. Assuming a normal distribution, what are the chances of my success? (Try using "artifacts" in Claude 3.5)

Read this performance review and suggest 3 concrete steps I could suggest for improvement and how we would measure them.

Using this data, create an analysis/recommendation/strategy...

Review our current list of products and this data about our customers. Come up with 20 ways/products/services/ideas for how we could both leverage our existing expertise/products/supply chain and customer trust to grow our business. Each item should be different with a description of only 40-60 words.

Data Visualization

Create an image that ranks sales by store location, experience of staff and time of day.

Turn this excel spreadsheet (or a page of data) of my department financials into a dashboard.

Write code to test, graph or visualize this model.

Use this feedback to generate a chart that shows the progress or failure of this strategy.

Finding Materials & Examples

Find me # relevant videos appropriate for audience A on subject B that are #-# minutes in length and give me a summary for each that includes its content, reliability and source.

Assemble real documents/ innovative examples/data concept X from the news/TikTok/YouTube/company website to make point X.

Create a scenario...

Find me 5 examples from company history where we altered course/reevaluated our strategy/expanded our market/grew profits...

Simulations & What if...run experiments, test ideas, make suggestions, forecast futures

How might we fully automate this process?

Generate scenarios from this data for how we might reach this sales target.

Using only CDC/government data, predict how might more X reduce the usage of Y?

Reimagine my pitch/this product for an Asian American audience and summarize what might need to be changed.

Help me stress test the attached business plan by simulating how our business might evolve over the next 2 years. I will play the CEO. You will simulate and describe economic, market and political challenges that might interfere with our plan. Every quarter you will update me and ask me to respond to new events and circumstances. You will then assess my actions and describe how the plan must change as a result.

(Variation 2) Predict what make happen over the next 3 years as we execute this business plan.

(Variation 3) Using the attached business plan, simulate how our business X might evolve over the next 20 years. I will play the CEO. You will simulate and describe economic, market and political challenges and changes. Every year you will update me and ask me to respond to new events and circumstances. You will then assess my actions, the describe how the corporation and business change as a result.

Here is an example of how an AI simulation might enhance the doing in education. Typically, a teacher who wanted students to learn about how a President might affect the economy, he or she would lecture about the macro-economics and constitutional limits on executive power. Instead try pasting this prompt into your favorite AI (Claude 3.5 or GPT 4o are both fast and free at the moment) and instead practice BEING the US President for a term.

Create a presidential simulation game about the relationship between the economy and actions of the US President. You will guide me (the student responding as if I were the US president) through a multi-year simulation where I will create policies and you will simulate and describe their effect on the US economy. Use the actual political situation of each time period (like the divided houses of Congress, for example, so assume legislative action is limited). Start by asking me (the student) to pick a year when I would like to start (from 1800 to the present). Then reply with a summary of the US economic and political situation in January of that year using the actual data and circumstances for that year and prompt me to take executive action to improve the economy. If I am stuck and ask for suggestions, then you can propose several choices. Do not allow me to propose action which is not constitutionally or legally possible for the President of the United States (who is only the executive and cannot create new laws and does not control the Federal Reserve, for example). Point out if my proposed actions exceed US Presidential power and cite the sources for these limitations. Do not make suggestions unless I get stuck or ask for them. Vary the types of choices you offer so I will get a sense of the variety of Presidential powers in relationship to the US economy. Once I have suggested a possible US Presidential action, assess my strategy and describe how the US economy would change as a result over the next three months. Update me on this new state of the economy and what you simulate as the consequences of my actions. Prompt me again to take action and repeat this process. Continue with this sequence of prompting me to take action and then describing the consequences, advancing the time every three months for up to four years total. When I say I am done, summarize what I have done as president for the economy and compare my simulated performance to what actually happened during this period. Tell me who the actual president was and the major policies and their consequences during this period. Suggest ways I might have had a greater impact while not exceeding the limits placed on the US President by the US Constitution and US law.

(Try this with Claude 3.5 and GPT 4o and compare to other models.)

Design Thinking

ROLE PLAYING & EMPATHY INTERVIEWS: I am trying to gain a richer understanding of problem X. You will help by responding as a trusting and honest potential customer/a Y person/expert in Z/average A to help deepen my knowledge. Question my assumptions when necessary and tell me stories to build my empathy for the real causes of this problem.

ANALYZE PATTERNS: Analyze and identify the key themes or problems from this product feedback/online reviews/interviews/oral histories/narratives/stories...

SEEING THE FUTURE: Twenty years from now, how will the assumptions about problem Z have changed? What new approaches or technologies will be available?

REFRAME THE PROBLEM: Reframe my formulation of the problem into ten radically different "how might we..." problem statements that center how we might frame what needs to be designed or built to create a new solution for humans.

BRAINSTORMING: Imagine 50 new and different ways we might solve problem X. Use data Y or template Z. OR Using examples from X, create 500 new products and write descriptions. OR List 20 potential problems with our thinking/assumptions about this idea/product/service. OR Give me 10 different ideas for a new/improved product/business/service/process that combines these ideas/concepts/problems and costs less than $/will be attractive to this market/is not currently available etc. (MORE BELOW in CREATIVITY)

TESTING: How might audience X react to this idea/product Y? Provide thorough and constructive feedback. What will they like most? What will they hate most? What would they change? How could I improve this idea/product?

AI for NUDGING

SPECIFIC EXAMPLE: You are an expert in nudging and behavior modification. Inspired by the ideas around libertarian paternalism and research in the book Nudge: Improving Decisions about Health, Wealth, and Happiness by Richard H. Thaler and Cass R. Sunstein (published in 2008 and revised in 2021), you use psychology and behavioral economics research to engineer choice architecture to nudge American citizens to alter their behavior in a predictable way that will encourage more voting without restricting options or significantly changing their economic incentives. You understand that the best nudges require minimal intervention and are cheap. Help me come up with new nudges to encourage American citizens to vote in elections beyond the Presidential elections. Start by creating 20 new ideas to change processes or choice architecture.

I note that Claude pushes back and needs to be further encouraged with: "Why are there ethical concerns about encouraging citizens to vote in a democratic society? That seems an essential component of equity."

CUSTOMIZE THIS VERSION: You are an expert in nudging and behavior modification. Inspired by the ideas around libertarian paternalism and research in the book Nudge: Improving Decisions about Health, Wealth, and Happiness by Richard H. Thaler and Cass R. Sunstein (published in 2008 and revised in 2021), you use psychology and behavioral economics research to engineer choice architecture to nudge customers/employees/citizens to alter their behavior in a predictable way that will encourage X without restricting options or significantly changing their economic incentives. You understand that the best nudges require minimal intervention and are cheap. Help me come up with new nudges to encourage X to do Y. Start by creating 20 new ideas to change processes or choice architecture.

Job Search

Describe the culture/reputation of company X have as reported by the people who work there.

What questions should I be sure to ask other employees/management about company X when I interview with them?

Read this job description and compare it to available information about [the company] strategy and culture, what are the priorities/personality traits/job experience/values/skills that are really important here? How should I focus my application?

Pretend you are VP X at company Y. Read this position description, my cover letter, resume and these emails/company strategy from VP X. How might X react to my materials? List missing elements and suggest ways for me to improve my application.

Read this position description, my cover letter, resume and these emails/company strategy from X. What are some interview questions that I should expect for this job/company/location/date?

Interview me for job A as if you were X.

You are typical hiring manager at company X for a leadership position. Read my cover letter/resume and help me improve my answers to standard interview questions, like with what is your greatest weakness and what did you learn from a failure.

COMMUNICATION and RELATIONSHIPS
Communication & Predicting Responses

Create a kind and caring but firm no response to this email.

You are an experienced marketing specialist. Generate a professional but enthusiastic description of this product/service/project or service that is targeted to an X audience. Use this description as a starting point. Develop innovative new ideas to reach new customers in market Y.

Pretend you are X with an open position. Read the uploaded position description, my cover letter and resume. How might X react to my materials? List missing elements and suggest ways for me to improve my application.

What might an average reader/customer/IRS auditor/Latino audience find confusing/objectionable/interesting?

Analyze these successful advertisements/initiatives and identify common elements, ideas, methods, structures, or language that might have contributed to their success. Recommend how I might adapt my current proposal to be more successful.

Give me feedback from a range of different types of customer from different political/academic/social backgrounds. How might they misunderstand my intentions?

Act as an experienced writing editor that is focused on writing that is easy to read and understand. Transform this email into one that will be easier to read but still have a professional tone for a team/manager/client. (OPTIONAL Use these examples of my other writing to mimic my voice and tone.) Shorten it by at least half. Start with a brief explanation of why the issue in the email matters. Provide clear navigation with bullets or numbers as necessary. Put the most important information at the top. Make it easy to respond by providing a clear call to action. Limit the response needed to one or maybe two things. Make sure there are no grammatical or spelling errors.

Tutor and Coach

How would you explain this to a beginner/non-expert?

Explain this to me using a soccer/legal/tech analogy

Explain this passage/concept by creating scenarios and personalized examples

Why did my boss object to this solution? I would like you to act as my personal tutor and teach me about subject X. Start by asking me a question that helps you gauge my level of understanding

Create feedback that will challenge me. Include feedback with inaccurate information and feedback that looks like a compliment but really is not.

Act like a friendly but experienced project manager. Read my plan and lead me through a dialogue that will challenge my perspectives. Ask me one question at a time to help me anticipate problems and refine my plan.

Role-Playing and Dialogues

Respond as if you were my boss during my performance review...

Pretend you are VP X at company Y. Read this position description, my cover letter, resume and these emails/company strategy from VP X. How might X react to my materials? List missing elements and suggest ways for me to improve my application.

Now interview me for the job as if you were X.

Respond as my boss A as I ask for a raise. Here are her website and emails.

Act as a devil's advocate/booster and present counter arguments/connections to company goals to the ideas in our meeting.

You are a hiring manager. Interview me for a job at X.

You are a busy venture capitalist (act like Mark Cuban on Shark Tank), and I am an entrepreneur looking for funding from you. Ask me to make my pitch and then ask me questions about my idea.

Respond as if you were my employee X and we have had this email exchange. Help me practice talking to you about Y.

A.I. as Mentor

Respond like an experienced and supportive [gender, race, discipline, background] executive and mentor. Read my resume, LinkedIn, performance reviews, 360 and X. Look at [local, region, company, national, international] job openings, leadership opportunities, and my goals, and consider these personal circumstances Y. Lead me through a dialogue that will help me decide what to do in this situation Z. Ask me one question at a time and respond to help me learn what I should do.
A.I. as Team Leader or Project Manager

Act as our team coach and prompt us with questions to discuss how could learn about our collective strengths and work together as an effective team.

Provide guidance that will help us ensure that all team members contribute equally to this project.

Act like a friendly but experienced project manager. Read my plan and lead me through a dialogue that will challenge my perspectives. Ask me one question at a time to help me anticipate problems and refine my plan.

Different members of our team want to proceed in different directions on this project. Read the individual proposals and provide a summary of where they overlap and where they do not. Read the assignment instructions, and provide a neutral compromise for how we can move forward.

Get Feedback from Different Readers and Perspectives

You are a kind but sensitive average reader/customer from culture/group/background Y. You often get confused. Read X and help me simplify things to make everything in this writing clear.

You are a scrupulous and experienced manager with no tolerance for lack of evidence. Focus on making this writing more persuasive and powerful.

You are a disagreeable skeptic/customer/reader from group Z. List all of the counterarguments and flaws in my position and respond as if you were a critic on social media.

You are an innovative writer. Offer critical feedback to help me improve this writing. Look for new connections, arguments and observations I may have missed. Your tone is warm and you are also wildly speculative, creative and fun.

You are a typical reader of X type of reports/writing. Offer me helpful and direct suggestions to make this work more agreeable to you.

You are a deeply conservative/liberal X from Y. Create a detailed and clear list of all of this things you find objectionable in this project/writing/work.

You are a technical specialist with expertise in X. Offer suggestions to improve the accuracy and clarity of terminology and concepts in this work. You are warm but a stickler for details.

You are [demographic profile X] and easily bored. Help me make this pitch more engaging.

Creative Quantity, Innovation and Problem Solving

Using examples from X, create 500 new products and write descriptions

How could I broaden the appeal/market for this service/product?

How could I reimagine this product/service for a new audience/circumstance/climate?

List 20 potential problems with our thinking/assumptions about this idea/product/service.

Give me 10 different ideas for a new/improved product/business/service/process that combines these ideas/concepts/problems and costs less than $/will be attractive to this market/is not currently available etc.

Create 50 new ideas for what we might do about this problem or in this situation?

This next more complicated prompt is adapted from Meincke, Lennart and Mollick, Ethan R. and Terwiesch, Christian, Prompting Diverse Ideas: Increasing AI Idea Variance (January 27, 2024). https://ssrn.com/abstract=4708466 or http://dx.doi.org/10.2139/ssrn.4708466 Generate new product ideas with the following requirements: The product must be a physical good/service/software that could be sold at a retail price of less than X USD 50. The ideas are just ideas. The product need not yet exist, nor may it necessarily be clearly feasible. Follow these steps. Do each step, even if you think you do not need to. First generate a list of 100 ideas (short title only) Second, go through the list and determine whether the ideas are different and bold, modify the ideas as needed to make them bolder and more different. No two ideas should be the same. This is important! Next, give the ideas a name and combine it with a product description. The name and idea are separated by a colon and followed by a description. The idea should be expressed as a paragraph of 40-80 words. Do this step by step!

Iteration

Write two different opening paragraphs for this proposal.

ADD: Find a good story/example or something more unexpected to hook the reader.

ADD: Write in a [type] style as if were [person/position]. OR Write specifically for this audience: the C-suite at company X/the HR team at Y/my boss Z.

ADD: Let me know if you need anything else from me before you begin

OR Slow down and think more carefully about the opening hook

Making Your Prompts Better

Task - Explicit Verbs: Elaborate, Reimagine, Explore, Invent, Create

Format - Email, Jargon-Free Summary, Syllabus, Code *Length or number (500-words, 50 new ideas)

Voice - Using X language *In the style of... Respond as if you were...

Context - Use/read/follow/imitate these models/examples I'm trying to be serious and funny at the same time

AI is changing Work and Thinking (Every job has a task that is going to change.)

AI is changing Average

AI is changing Creativity

AI is changing Strategy

Order books at Amazon https://www.amazon.com/Teaching-AI-Practical-Guide-Learning/dp/1421449226

or use discount code HTAI24 at Johns Hopkins for 30% off h ttps://www.press.jhu.edu/books/title/53869/teaching-ai

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For a pdf of all slides and citations: click here THE AI ECOSYSTEM: Click to open AIs and AI tools in multiple tabs in your browser. PROPRIETARY FRONTIER MODELS: These are different AI models. They...

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For a more detailed understanding of the inner workings of generative AI, you can read this detailed primer from Educause.

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Understanding the current state of technology requires understanding its origins. This reading list provides sources relevant to the form of generative AI that led to natural language processing (NLP) models such as ChatGPT.

Credit: Qpt / Shutterstock.com © 2023

Generative artificial intelligence (AI) is in a renaissance amid a profusion of new discoveries and a breathless frenzy to keep up with emergent developments. Yet understanding the current state of technology requires understanding its origins. With the state of AI science changing quickly, we should first take a breath and establish proper footings. To help, this article provides a reading list relevant to the form of generative AI that led to natural language processing (NLP) models such as ChatGPT.

Artificial neural networks have been around for more than sixty years. We can start in 1958, when the perceptron was initially implemented on a computer. For the first time, an algorithm used computing power to ingest labeled sets of data and classify them into categories. Nearly thirty years later, in 1986, backpropagation was introduced as an algorithm for training a neural network to learn from its mistakes. This advancement enabled multilayer perceptron networks to learn in nonlinear ways. In 2017, the Transformer was created as a neural architecture that improves on the memory limitations of past recurrent models. This efficiency gain enables a network to better handle the context and relevance of the information it has been provided. All three of these technologies remain relevant and used today.

There is a deep historical record about practical developments in neural networks, and the theoretical work supporting them includes centuries of rigorous discoveries in math and biology. Interestingly, despite this rich context of empirical and conceptual work, neural networks have not received much popular attention. AI developments have broken into the collective consciousness only occasionally-for instance, when the IBM supercomputer Deep Blue beat world champion Garry Kasparov in a chess match in 1997 or, more recently, when Google DeepMind's neural network AlphaGo defeated Go grandmaster Lee Sedol in 2016. Instead of AI developments, computer and data networks have commanded most of the attention of technologists in the past few decades. While AI has been relevant and useful in our lives for at least the past ten years, the barrier to entry has been high, and its applicability to higher education has not been obvious. The time has come, however, for IT professionals to focus on AI in its various forms, not because it will solve all problems but because it will transform how organizations operate.

Neural Network Basics

Since this is a reading list, we'll start off with a video! How could we do such a thing? Because at this stage, only a human author can make such a contradictory statement. If we must condense decades of knowledge about neural networks, then it is necessary to focus on the fundamentals. Video is a helpful tool at the outset because neural networks are fundamentally visual. This is partly because neural networks are based on visual math (e.g., calculus, statistics, and linear algebra) and also because the multiple layers of networks are inherently multidimensional concepts.

Produced in 2018 by 3Blue1Brown, the video "But What Is a Neural Network? | Chapter 1, Deep Learning" occupies an important moment in time: before Transformers began to accelerate NLP models. It presents a classic challenge in AI development: how to handle recognition of handwritten numbers. This video articulates the fundamental components of modern neural networks including the concept of supervised learning. It also provides an example of the conceptual problem presented by hidden layers.

Now that we've seen a general representation of how a neural network functions, it is time to expand our horizon: there are many types of neural networks, each with particular strengths and weaknesses. Fjodor van Veen's list, "The Neural Network Zoo" (The Asimov Institute, September 14, 2016), will at first seem overwhelming, but the goal in reading about these is not to commit this information to memory. Instead, consider each network permutation as being illustrative of the many possible tools that can be used to solve different problems.

The first category of networks that we need to understand in greater detail consists of recurrent neural networks (RNNs), developed in the 1980s. RNNs allow for nodes to interact with each other, enabling the network to handle sequential data (e.g., words in a sentence must be in sequence to be understood). The interaction of nodes within the hidden layers enables the network, with sufficient training and fine tuning, to predict future steps in a data sequence. Eniola Alese's article "Understanding RNNs Using the Game of Chinese Whispers" ( Medium, April 13, 2018) is a helpful introduction to the subject.

In "And of course, LSTM - Part I: The ABC's of the LSTM" ( Medium, June 8, 2018), Alese introduces some of the challenges with RNNs-namely that they have memory problems. In 1997, the long short-term memory (LSTM) neural network was developed, solving this problem. LSTM introduced a memory state to enable the network to retain relevant information in order to understand context, while discarding information that is no longer needed.

While LSTM networks can handle much larger inputs, they are slow to train, and they are less efficient running on modern GPUs that are designed for parallel processing. Despite these limitations, LSTMs continue to be relevant and useful for some tasks (as are RNNs). Two decades later, Transformers were created to address some of these limitations. This technology has largely driven the current revolution in NLP. Transformers have some powerful advantages, including the ability to better understand the context of input data due to a self-attention mechanism that is more precise about word dependency and relevance. Jay Alammar provides a high-level conceptual overview in "The Illustrated Transformer" (June 27, 2018), whereas Peter Bloem gives a nice introduction from a programmer's perspective in "Transformers from Scratch" (August 18, 2019).

Definitions

Note: The definitions listed here are for concepts mentioned in the article. They do not cover all aspects of neural networks.

Fundamental Components

Hidden Layer: A generic term for all layers between the input and output layer. Different configurations of layers and neurons result in different types of neural networks.

Neuron: A multi-layer neural network node that receives multiple inputs with distinct parameters, which are processed through an activation function to produce various types of output.

Parameters: The weights and biases associated with each neuron in a network. These constitute the primary means to tune network functionality and behavior through training.

Perceptron: A single-layer neural network node that receives information (input) and, with the help of an activation function and parameters, produces a binary output.

Neural Network Types

Artificial neural network: A type of software that can independently learn at the expense of reduced transparency. In traditional software, a programmer defines fixed parameters and designs a structure based on logic. Neural networks, like other machine learning algorithms, are not deterministic in this way. Instead, they are probabilistic: they can handle novel tasks without explicitly being designed to do so. Training determines how the software operates, which means that a given neural network can behave very differently based on how it is trained.

Long Short-Term Memory (LSTM): A general improvement to the RNN design. Nodes can retain relevant, specific information from previous nodes' outputs. This is made possible with a dedicated memory mechanism that is discerning (based on training) about what information should be retained, discarded, or output at a given point in time. These networks are able to handle complex sequential data (e.g., several paragraphs of text).

Recurrent Neural Network (RNN): A network design in which nodes within the hidden layer receive partial information about what was processed by previous nodes. This allows for a type of short-term memory wherein a given node may have condensed information from a previous node's outputs. When combined with new input, this enables the network to make predictions about sequential data.

Transformer: A neural network design that ingests all the input it receives simultaneously, analyzes that information to determine which parts are the most relevant (self-attention), and then uses a basic feed-forward network to determine output. The problems of limited memory caused by handling input in a rigid sequential order (e.g., LSTMs and RNNs) are solved by a holistic understanding that enables the transformer to handle highly complex data types.

Neural Network Training

Backpropagation: Developed for supervised learning, an algorithm that computes gradients (essentially efficiencies) in accord with a loss function and propagates these calculations back through all network layers, adjusting parameters in order to improve network accuracy.

Few-shot learning: A variation of supervised learning in which very little labeled data is provided to the neural network. A sufficiently sophisticated neural network can interpret a well-curated limited dataset and accurately extrapolate future matches for data and labels it has not seen in training.

Reinforcement Learning from Human Feedback (RLHF): A training technique that can be used independently or in combination with other training techniques. The user helps train the behavior of the neural network by rewarding some responses over others, thus influencing the way the network modifies its own weights and biases.

Supervised learning: An ideal training technique for neural networks that are designed for well-defined datasets. Engineers will curate a set of data with labels, which in effect is like providing the questions and answers for a test. When the neural network processes this data, it adjusts its parameters based on its own results. If it succeeds in correctly labeling the data itself, certain connections between nodes will strengthen; if it fails, those connections will weaken.

Unsupervised learning: A quick way to train a network on things like grammar and knowledge without explicit labeling. Engineers curate a set of data and ensure that it is relevant and clean, but no labels are provided. The neural network produces value in this scenario when it is able to successfully identify meaningful patterns in the data. A loss function is used but, in this case, for optimization of the network rather than accuracy per se.

The Building Blocks of ChatGPT

With some history established, we can now review important articles published in the past few years. To start, note that OpenAI (the creator of ChatGPT) was a nonprofit organization founded in 2015 by well-established industry leaders with the intent to freely collaborate on AI. In June 2018, OpenAI published "Improving Language Understanding with Unsupervised Learning," which makes the case for unsupervised learning using Transformers. Unsupervised learning is the process of providing data to a neural network without telling the network how to understand the data (e.g., giving the network images of animals without labeling them) and configuring the network so that it can figure out, on its own, what is needed. In the case of GPT-1, 7,000 books were fed into the model to train it to accurately predict future words.

Eight months later, OpenAI announced GPT-2. This version demonstrated capabilities that both exceeded expectations and demonstrated emergent capabilities: ways of operating that the developers had not anticipated. This led OpenAI to do something that didn't garner a lot of attention outside of AI researchers: it restricted the release of GPT-2 due to fears of malicious use (e.g., generating spam, producing fake product reviews). In May 2019, a limited version was released, followed in August by a model at something like half its capability (measured in parameters), and in November 2019 the full GPT-2 model was released. Once again using very accessible language, OpenAI describes GPT-2 in "Better Language Models and Their Implications," which includes a section on policy implications and thus begins to grapple with the societal impact of this technology.

Six months later, in June 2020, OpenAI released GPT-3, without any staged release or restrictions to capabilities. Instead, OpenAI decided to create a for-profit arm and refrained from releasing the source code to GPT-3. The key breakthrough in GPT-3 is that after increasing the size and configuration of the model, OpenAI discovered that the model was now capable of few-shot learning: performing supervised learning with very limited data, enabling rapid adaptation. Thus, while the previously discovered practice of unsupervised learning continued to be followed in GPT-3, the supervised learning portion of training was dramatically improved. Unfortunately, OpenAI did not provide the same high-level summary as it had for GPT-1 and GPT-2, but it did make up for this change of tack with a very thorough technical paper: Brown et al., "Language Models Are Few-Shot Learners" (July 22, 2020). While this paper requires some knowledge in the field, it explains the relevance of this technological advancement.

In March 2022, OpenAI released GPT-3.5, with GPT-4 following just a year later, in March 2023. The chatbot product known simply as ChatGPT-launched on November 30, 2022-for the first time provided an easy, web based interface to the GPT-3.5 model. Something that is notable with these releases (particularly starting with the November 2022 reconfiguration of GPT-3.5) is the dramatic increase in emergent capabilities. This time, OpenAI did not publish a high-level overview for ChatGPT. Instead, the web page "Introducing ChatGPT" simply describes what the developers saw as some neat but essentially "iterative" changes.

In February 2023, Stephen Wolfram, a pioneer in the development and application of computational thinking, wrote a very thorough blog post: "What Is ChatGPT Doing . . . and Why Does It Work?" Wolfram summarizes how ChatGPT works. It is a long read with some math, but it is an outstanding resource.

Meanwhile, AI researchers became increasingly excited and concerned about what GPT was capable of. They started to notice that entirely new capabilities were possible. Wei et al. described this phenomenon in "Emergent Abilities of Large Language Models" ( Transactions on Machine Learning Research, August 2022). The co-authors noted societal risks, including behavioral and sociological concerns.

NLP Model Ethics

NLP models like ChatGPT raise a variety of ethical questions. While OpenAI has operated with intentionality with respect to ethical concerns, monetary and corporate interests likely mean that the company's ethics are not guaranteed. Considering the exploding growth of alternative generative AI models, however, many of these products will face much less scrutiny and may have far more significant ethical lapses. But before we can discuss ethics particular to AI, we should consider an ethical framework. Hegel's theory of responsibility is a helpful mooring: social context is essential for interpreting ethics, and for any given action, it is also important to consider responsibility for the consequence of this action. Further, knowledge of consequences is imperative for interpreting responsibility.

Starting with the ethical question of bias, OpenAI has put in significant work to train the GPT model to be mindful of social context and to manage for bias. This is quite unlike Microsoft's infamous 2016 chatbot Tay, where the problem of AI bias took on an extreme form. The relatively positive results of GPT demonstrate the importance of supervised training. A pressing concern is that competing models have not been trained as intentionally; in addition, "desktop" generative AI models, which are accessible to anyone, could become unethical by design or accident. In "Language (Technology) Is Power: A Critical Survey of 'Bias' in NLP" ( ACL Anthology, July 2020), Blodgett et al. provide an excellent critique of bias research in NLP models and make a compelling case for why much more work is needed to improve bias recognition and training in NLP. In "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" ( FAccT '21 Proceedings, March 2021), Bender et al. address ethical concerns about increasing NLP models to sizes that may amplify bias and create adverse environmental impacts.

Misinformation is another significant concern. Generative AI models, including GPT, "hallucinate" or confabulate by making up information. This is particularly jarring because the confabulations can appear authoritative: made-up sources sometimes reference real authors but fake titles and even specific page numbers. Such faults can be addressed through supervised training: try asking an NLP model about the company that produced it, and the results will be very consistent. We have also seen GPT's dramatic reduction in confabulation errors as new releases come out, thanks in part to reinforcement learning from human feedback (RLHF), in which users rank responses in terms of accuracy. Meanwhile, a product like Bing Chat has attempted to address this issue with a "creativity" setting with the intended goal of confabulation. This is an ethical position that requires nuance, since it posits that when an NLP makes up information, it is being creative rather than misleading. In "Training Language Models to Follow Instructions with Human Feedback" (March 2022), Ouyang et al. make a compelling case for using RLHF in NLP.

A similar consideration is the impact that this technology has on society. We continue to struggle with the ethical impacts of social media, a technology that is also driven by AI. Since this different form of AI has been used to disseminate fake information, it is apparent that NLP models can be used in a similar manner. Social media AI is, ironically, designed around controlling our attention. One of the consequences of controlling attention is algorithms that promote extreme viewpoints to keep users engaged. This, in turn, leads some people to create socially siloed cultures. When NLP bots are pervasive and anonymized across platforms, how will that change how we interact with each other? What does it mean for our understanding of reality when so much of our society is digital and when the digital output of AI is indistinguishable from the digital output of a human? Will NLP models take a step further than social media and create a distinct AI culture? Hunt Allcott and Matthew Gentzkow provide a thorough account of the role of social media in influencing different kinds of social behaviors in their article "Social Media and Fake News in the 2016 Election" (Journal of Economic Perspectives, Spring 2017).

Privacy and security are complicated ethical concerns for NLP, since we understand very little about the possible consequences of emergent capabilities. Spam and phishing are obvious concerns: grammar mistakes will no longer be a telltale sign of fake solicitations. There are also immediate concerns about the data directly shared with the model (and breaches have already occurred). Yet the ethical questions run far deeper. To what degree can exposure to limited information about a person allow for excellent impersonation of that person, rendering so much of our security infrastructure invalid? To what extent can a model learn about an individual and accurately predict how that person thinks and acts in order to manipulate someone to take an action? How much information will be necessary to determine someone's relationships, habits, and preferences in ways that surveillance states could leverage? If the business model of social media AI is advertising (and thus, AI is influencing people to buy products), what business models will emerge for NLP? How will the mechanism of autonomy on the internet, sometimes used by humans in problematic ways, allow an effective mechanism of responsibility for AI? Indeed, autonomy and privacy are likely on the verge of reformation. Brundage et al., The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (February 2018), provides a comprehensive overview of various security problems posed by AI. While the report is long, it is essential reading and provides a framework that will hopefully be updated in the future.

The crux of AI ethics is accountability and transparency. A key component in neural network functionality is the hidden layers that lie beyond the direct control of programmers. When emergent behavior happens, the practical result is that a black box effect has been extended to the output layer: unanticipated behavior or capability has been realized. There is an important ethical distinction between not knowing how something is done and not knowing what the network is able to do. For every novel permutation of different outputs, we have no mechanism to evaluate the ethical implications of the consequences that may follow. Whether we follow Hegel or other ethical philosophers, understanding how these models produce specific results and understanding the full spectrum of what they are capable of producing is essential.

Technologists looking to use NLP models within their institutions need to establish an ethical framework that considers those concerns within their realm of agency and control. One step toward transparency is attribution: how and when do we attribute work to generative AI? Looking up a topic up on the internet, getting ideas, and writing about it is very different from copying sections from an article without attribution. Similarly, direct copying from content produced by generative AI tools requires disclosure, but using that content for ideation does not. Consider also the need for accountability. Blindly trusting in generative AI output is unwise and often unethical. AI output cannot be cited because it is not referenceable. Validating AI output by consulting reliable sources to arrive at a sound conclusion is surely reasonable. Ethical Guidelines on the Use of Artificial Intelligence (AI) and Data in Teaching and Learning for Educators (2022), published by the European Commission, is an excellent guide for the thoughtful use of AI in education.

Since I started this reading list with a video, how could I pass up the opportunity to end the same way? In the hour-long video "The A.I. Dilemma" (March 9, 2023), Tristan Harris and Aza Raskin, two cofounders of the Center for Humane Technology, excellently summarize the dangers currently posed by the AI field generally. They discuss what the future adoption of these tools may look like based on what we know now about the current capabilities of AI technology.

In Summary

The history of AI is entwined with the history of computing. While the importance of the internet is now widely accepted, the internet is a new technology by comparison with AI. The societal relevance of the internet languished for more than two decades after its creation, yet the incubation period for AI has been even longer.

AI is no longer a specialized or niche field of research with limited applicability. In the past decade, AI has been delivered, in one form or another, to consumers without the AI label (e.g., social media platforms, voice assistants, recommendation systems, photo editing). ChatGPT is an announcement to the world that the field of deep learning-that is, highly capable neural networks-is now immediately accessible to all. Perhaps more importantly, ChatGPT is a demonstration of the effectiveness of one type of AI tool that will encourage all of us to invest in and explore this expansive landscape of possibilities.

As many of us in IT organizations embrace AI tools in the coming years, we need to recognize the consequences of our actions. In time, AI will fundamentally challenge the way we understand reality. AI is a novel collection of technologies without historical parallel. Just as AI will not solve all problems, there is not one simple and ethical way to navigate AI. Instead, a rigorous analysis of ethics and metaphysics and a careful consideration of how IT organizations deploy AI will be required. IT organizations need to build on the ethical framework of equity and inclusion to ensure that this empowering technology experience of generative AI is transparent and accountable.

Notes

1. For the purposes of this article, neural networks throughout will refer to artificial neural networks.

2. The seminal 2017 paper "Attention Is All You Need" by Ashish Vaswani et al. gives an introduction to this subject. Despite the catchy title, this paper is quite technical and requires significant working knowledge of deep learning. Note that Google pioneered this Transformer technology and made it open source, along with other key AI technologies.

3. A. Ivakhnenko and V. G. Lapa's book Cybernetic Predicting Devices (Kiev, 1965) was developed in the Soviet Union and was the genesis of deep learning. For the first time, a mathematical foundation was established for how neural networks could successfully operate with multilayer perceptrons. While this is a technical work, in Chapter 4 the authors recognize the importance of Frank Rosenblatt's 1958 work with implementing perceptrons, and they provide a great contemporary understanding of the model. Following up on this work was Ivakhnenko's article "Heuristic Self-Organization in Problems of Engineering Cybernetics," Automatica 6 (1970), in which he introduced an algorithm to automate neural network construction and utilized the theoretical groundwork he had previously established with multilayer perceptrons.

4. A wonderful resource for more information on other types of networks is the blog series on deep learning written by Tim Dettmers, a staff member at NVIDIA, in 2015-2016. The first blog post focuses on visual AI (e.g., how convolutional artificial neural networks function). The second blog post provides a history of deep learning and discusses some of the math developments. The third blog post covers some of the fundamental building blocks of NLP models. The fourth blog post discusses different network training methods of the time.

5. This high-level summary of their work is quite accessible to nonexperts. For the technical version, see Radford et al., "Improving Language Understanding by Generative Pre-Training" [2018].

6. For the technical version, see Radford et al., "Language Models Are Unsupervised Multitask Learners" [2019]. An interesting way to look at this paper is to search for the term surprising, as noted by Benj Edwards in "Why ChatGPT and Bing Chat Are So Good at Making Things Up," Ars Technica, April 6, 2023.

7. The March 2022 initial release of 3.5 received similarly modest treatment by way of feature announcement.

8. Ethical philosophy is absent in many publications on AI ethics, and this often results in shallow ethical discussions. Furthermore, since 2021 several ethicists with robust philosophy backgrounds have been fired by both Google and Microsoft. See Daisuke Wakabayashi and Cade Metz, "Another Firing among Google's A.I. Brain Trust, and More Discord," New York Times, May 2, 2022; Zoe Schiffer and Casey Newton, "Microsoft Lays Off Team That Taught Employees How to Make AI Tools Responsibly," The Verge, March 13, 2023.

9. Georg Wilhelm Friedrich Hegel, Elements of the Philosophy of Right (1820). Hegel's thoughts on imputability are also relevant. Knowledge of what may occur as a result of one's actions is an important discourse when considering emergent behaviors in AI.

10. For a recent historical perspective, see Sofiya Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (2018), a groundbreaking book based on years of research about Google's search algorithms and the pervasive bias within them.

11. Oddly, for some famous confabulations that have occurred to date, companies disclaimed responsibility. This is a dubious ethical position because the only substantive function of NLPs is their output. This is like saying the company is responsible only when there is no problem! Since the summer of 2023, all major models now feature disclaimers about this issue.

12. Interestingly, ChatGPT works within an explicit knowledge cutoff date of September 2021. This is partly a practical matter, since pretraining has to begin and end at some point in time. It is also, however, intentional: OpenAI researchers understood that these models would soon be used to populate the internet with generated content and that training a neural network on content generated by other neural networks leads to model collapse. Similarly, if we end up "validating" the results of ChatGPT through web searches that are driven by other NLP models and lead to AI-generated content, does that really count as validation?

Brian Basgen is CIO at Emerson College.

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Understanding the current state of technology requires understanding its origins. This reading list provides sources relevant to the form of generativ

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Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality

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Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality

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22 Pages Posted: 18 Sep 2023 Last revised: 17 Mar 2026

See all articles by Fabrizio Dell'Acqua

Harvard Business School

Harvard University - Business School (HBS)

University of Pennsylvania - Management Department

Harvard University Lab for Innovation Sciences; Harvard LISH, Lab for Innovation Sciences; University of Warwick, Warwick Business School

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Boston Consulting Group, Henderson Institute

Boston Consulting Group, Henderson Institute

Boston Consulting Group

Harvard Business School - Technology and Operations Management Group; Harvard Institute for Quantitative Social Science; Harvard University - Berkman Klein Center for Internet & Society

Date Written: September 15, 2023

Abstract

We introduce and study the concept of a "jagged technology frontier" to describe the uneven impact of artificial intelligence (AI) capabilities, where AI assistance improves performance for some tasks but worsens it for others, even within the same knowledge workflow and with a seemingly similar level of difficulty. In collaboration with the global management consulting firm Boston Consulting Group, we have developed realistic management consulting tasks and examined the human performance implications of using AI to perform complex and knowledge-intensive work. The preregistered experiment
involved 758 knowledge workers. After establishing a performance baseline on similar tasks, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. For each one of a set of 18 realistic knowledge tasks within the frontier of AI capabilities ranging from creative to analytical tasks, subjects using AI outperformed those not using AI, completing 12.2% more tasks and completing them 25.1% more quickly on average while also delivering solutions of significantly improved quality. However, for a complex managerial task selected to be outside the frontier, subjects using AI were 19% less likely to produce correct solutions compared with those without AI, pointing to potential limitations of AI supporting knowledge workers. We discuss the positive and negative implications of AI-aided human performance in knowledge-intensive tasks.

Dell'Acqua, Fabrizio and McFowland III, Edward and Mollick, Ethan R. and Lifshitz-Assaf, Hila and Kellogg, Katherine and Rajendran, Saran and Krayer, Lisa and Candelon, François and Lakhani, Karim R., Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality (September 15, 2023). Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013, The Wharton School Research Paper, Organization Science, Forthcoming, Available at SSRN: https://ssrn.com/abstract=4573321 or http://dx.doi.org/10.2139/ssrn.4573321

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navigating the jagged technological frontier: field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality

# data[].attributes.attachment_link.title
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality

# data[].attributes.attachment_link.description
We introduce and study the concept of a "jagged technology frontier" to describe the uneven impact of artificial intelligence (AI) capabilities, where AI assist

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Inside Higher Ed | Higher Education News, Events and Jobs

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Most students outrunning faculty in AI use, study finds

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A new study finds over half of students use generative AI, while more than 75 percent of faculty members do not regularly use the technology.

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The impact of artificial intelligence on learner–instructor interaction in online learning

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The impact of artificial intelligence on learner–instructor interaction in online learning

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The impact of artificial intelligence on learner–instructor interaction in online learning - International Journal of Educational Technology in Higher Education

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Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors’ routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner–instructor interaction (inter alia, communication, support, and presence) has a profound impact on students’ satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner–instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner–instructor interaction, capturing students’ and instructors’ concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

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Majority of Grads Wish They’d Been Taught AI in College

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Majority of Grads Wish They’d Been Taught AI in College

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Inside Higher Ed | Higher Education News, Events and Jobs

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New report finds recent grads want AI to be taught in class

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A new survey shows 70 percent of graduates think generative AI should be incorporated into courses. More than half said they felt unprepared for the workforce.

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InnovateOhio AI Toolkit: Resources for Teachers

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InnovateOhio AI Toolkit: Resources for Teachers

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The above link, although it appears to be broken, does function! Click above to access InnovateOhio's toolkit for teachers, which is not specific to higher education but includes a rich plethora of introductory material, information on potential applications, and practical templates and tools.

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AI EduCraft Series: Revolutionizing Teaching and Research

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AI EduCraft Series: Revolutionizing Teaching and Research

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RevolutioniZIng Teaching and ReseaRch

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Welcome to the AI EduCraft Series, a micro-workshop series tailored for faculty aiming to build their understanding and application of Artificial Intelligence (AI) within higher education. Throughout this series, we will focus on practical aspects, developing AI literacy to efficiently integrate these technologies into teaching methodologies and research practices. The sessions will dissect the impacts of AI on both teaching and learning, exploring its role in reshaping assessment methods and addressing ethical considerations.
Workshop Series Highlights
Session 1: AI Superpowers for Educators: How to Use AI to Boost Your Teaching and Research Increasing Efficiency: Streamline tasks and administrative duties with AI applications, tailor educational content to meet the unique needs of students using AI.

Session 2: AI and the Future of Assessment: Explore how AI can transform assessment methods for a more engaging and effective evaluation process.

Session 3: AI, Academic Integrity, and Ethical Considerations: Ethical dimensions of AI, focusing on maintaining academic integrity and responsible AI adoption.

Session 4: Preparing Students for the Future: Acquire practical AI skills to better prepare students for the evolving job market influenced by AI.
Workshop Series Objectives

AI Literacy Enhancement: Grasp fundamental AI concepts and apply them to educational settings, Evaluate and integrate AI tools into teaching practices.

Teaching and Learning Impact: Identify practical opportunities to improve teaching methodologies through AI integration, Develop strategies for implementing adaptive, personalized learning experiences.

Research Advancements with AI: Explore how AI can enhance and expedite research processes in psychology, Utilize AI tools for data analysis, hypothesis testing, and knowledge discovery.

Understanding the Higher Education Landscape: Analyze the role of AI in shaping the future of higher education, Assess the challenges and opportunities presented by AI in academic institutions.

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"The Post-AI Instructional Designer" (Dr. Philippa Hardman) 

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"The Post-AI Instructional Designer" (Dr. Philippa Hardman) 

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In this article, Dr. Hardman describes how AI and the organic intelligence of faculty & instructional designers can be harnessed to design academically excellent classes.

Dr. Hardman's Substack newsletter, "Dr Phil's Newsletter, Powered by DOMS™️ AI," covers this topic in-depth--subscribe if you're interested!

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Hey folks! 👋

An increasing amount of research shows that optimal workplace performance happens when we combine the powers of expert human and AI.

The big question I've been exploring this week is: what should a partnership of human instructional designer and AI look like in practice, and what does this mean for our key skills, roles and responsibilities?

According to this research, humans and AI have distinct roles. As a result, the relationship between AI and instructional designer is one of task separation and delegation: the human does what they're good at, and the AI does what it's good at.

Research shows that this "Delegation" model enables instructional designers to make some significant gains in efficiency (speed) and smaller but still measurable gains in effectiveness (quality of output).

However, according to a growing, parallel body of research - including a study from the University of Michigan published just last week - the most effective model of human-AI interaction (i.e. that which leads to most gains in both effectiveness and efficiency) is more nuanced and complex than simply dividing tasks between human and AI.

According to a growing body of research, including a study from the University of Michigan published last week, the line between the capabilities of human and machine may be more blurred than the Delegation Model suggests.

In an experiment involving school teachers, researchers found that optimal performance (efficiency and effectiveness) occurred when teachers used AI not for isolated task completion but for functional, creative and strategic input on their end to end workflow.

Specifically, the study revealed that teachers who reported most productivity gains were those who used AI not just for creating outputs (like quizzes or worksheets) but also for seeking input on their ideas, decisions and strategies.

Those who engaged with AI as a thought partner throughout their workflow, using it to generate ideas, define problems, refine approaches, develop strategies and gain confidence in their decisions gained significantly more from their collaboration with AI than those who only delegated functional tasks to AI.

So what would this model like in practice for instructional designers?

For illustrative purposes, let's think about what the Partnership Model looks like in the context of ADDIE:

Analysis:

AI: Analyses data, suggests potential learning objectives, provides insights on learner characteristics

Human: Collaborates with AI to define problems, conducts stakeholder interviews, refines AI-suggested objectives

Design:

AI: Proposes instructional strategies, generates ideas for learning activities

Human: Engages in dialogue with AI to refine strategies, combines AI suggestions with personal expertise

Development:

AI: Generates content drafts, suggests interactive elements, assists in storyboarding

Human: Co-creates with AI, iteratively refining content and incorporating AI suggestions

Implementation:

AI: Provides real-time feedback on engagement, suggests adaptive content changes

Human: Collaborates with AI to make real-time adjustments, uses AI insights for continuous improvement

Evaluation:

AI: Provides predictive analytics, suggests potential improvements

Human: Engages in dialogue with AI to interpret data, collaboratively develops improvement strategies

In the post-AI world, this role doesn't change, but the way that we execute it (and the skillset required to to optimise for efficiency and effectiveness) does:

Understand Instructional Design Theories and Models: the fundamental theories remain important, but the skill of prompt engineering and deep knowledge of instructional design theory becomes more crucial here, as IDs need to effectively communicate instructional design principles to AI tools and assess the quality of AI's suggestions.

In all of this change, the ID's role is elevated rather than eliminated. Among the most critical skills for the post-AI ID are:

An increasing amount of research shows that optimal workplace performance in general (and optimal instructional design performance specifically) happens when we combine the powers of expert human and AI.

While delegating "output tasks" to AI can increases our efficiency (i.e. the speed of the ID process), optimal results which elevate both our efficiency (speed) and effectiveness (impact) emerge when we adopt a Partnership Model of AI-human interaction, where AI serves as an "always-on" thought partner, contributing to functional, creative and strategic decision-making throughout the end to end instructional design workflow.

So the message is clear: adopting a Partnership Model with AI is good news for IDs. But are we, the human instructional designers, ready for this change?

On a practical level, the deep integration of AI in every aspect of our workflow requires us to overhaul our knowledge, skills and tooling; we need to develop a deep understanding of how AI works, how to work with AI (e.g. prompting skills) and how to assess and validate its outputs.

Perhaps more significantly, on a psychological level, the deep integration of AI requires us to relinquish some of our power and control over our workflow and outputs. It also requires a shift from a world where there is a definitive line and clear differentiation between the skills and capabilities of the machine and the skills and capabilities of the human.

Acknowledging that this line is blurred and reconfiguring what this means for our roles, responsibilities and key skills is a huge, existential step for instructional designers and the workforce more broadly.

Whatever the case, two things are clear: AI is here to stay, and IDs who work in partnership with it will lead the field in terms of their efficiency and effectiveness.

Happy experimenting!
Phil 👋

P.S. If you want to get hands-on and experiment with how to integrate AI into your instructional design process, check out my AI Learning Design Bootcamp.

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How the ID role is changing, and what this means for your key skills, roles & responsibilities

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In this course, you'll learn about generative AI, which is a type of AI that creates new content, such as text, images, or other media. You'll explore how to use generative AI tools to assist your teaching practice by saving time on everyday tasks, personalizing instruction to meet student needs, and enhancing lessons and activities in creative ways.

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Crafting AI Prompts: A Workbook for Educators

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Crafting AI Prompts: A Workbook for Educators

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Crafting AI Prompts: A Workbook for Educators [MAKE A COPY]

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Speaking of Higher Ed: Conversations on Teaching and Learning

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Speaking of Higher Ed: Conversations on Teaching and Learning

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15. AI in Higher Ed with David Joyner, PhD by Speaking of Higher Ed: Conversations on Teaching & Learning

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Should I discuss AI with my students? How can I benefit from using AI? In this episode of Speaking of Higher Ed, David Joyner, PhD, joins us to explore these questions and how AI is transforming education, for students and educators. ChatGPT generated significant discussion when it launched in November 2022. Now, with more than a year of growing in understanding about generative AI, we are discovering what changes are necessary in the classroom. Joyner provides valuable insights from his research and classroom experiences, addressing educators seeking basic knowledge about ChatGPT and those aiming to deepen their understanding of generative AI. Educators aren’t the only ones adapting to technologies like ChatGPT; students are also adjusting. This technology holds the potential to facilitate faster learning and improvement in targeted areas. However, it may require adjustments to assessments to maintain the desired level of rigor in evaluating student mastery of the material. We also discuss Joyner’s new publication, A Teacher’s Guide to Conversational AI, and the professional certificate program he developed, available on the edX online learning platform. Speaking of Higher Ed is produced by the Center for Instructional Innovation at Augusta University. Find more episodes and resources on our show page. Subscribe now so you don’t miss an episode
About David Joyner: Joyner is the Executive Director of Online Education & Online Master of Science in Computer Science program in the College of Computing at Georgia Tech. He has written more than 50 papers and authored three books on online learning and computer science education.

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Using Student Data to Bridge the AI Divide

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Using Student Data to Bridge the AI Divide

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Knowing your students is essential for bridging the AI divide and paving the way for a more inclusive and equitable future.

Credit: melitas / Shutterstock.com © 2024

Artificial Intelligence (AI) has become, at what feels like warp speed, an inescapable element in the dynamic landscape of higher education. Yet, as educators embrace the potential of AI to revolutionize teaching and learning, we must confront the stark reality of inequitable access and the need for ethical considerations surrounding its implementation. San Diego State University (SDSU), under the leadership of President Adela de la Torre, has undertaken a groundbreaking initiative to address these challenges. Through a comprehensive October 2023 survey of SDSU undergraduate and graduate students, the university shed light on the urgent need for data-driven strategies to ensure equitable access, foster responsible use, and empower faculty, staff, and students in navigating the AI landscape.

Moving to Data-Driven Decision-Making

The survey conducted by SDSU revealed much regarding how students experience AI, including comments regarding concerns about a gap in access to AI tools and a broader digital divide. Further, students who reported greater access to technological resources, as measured by number of smart devices owned, were more inclined to feel at ease with AI (see table 1). This suggests a critical need for higher education institutions to forge partnerships with AI vendors to negotiate fair pricing models that prioritize accessibility for all students. To this end, SDSU has established the Equitable AI Alliance (EAIA) as a collaborative, multi-institutional platform and consortium advocating for accessible AI solutions and sharing best practices.

Table 1. Device Ownership and AI Complexity
Students who agreed with the statement "AI is too complex for me to grasp"(n=7,025)

Central to addressing these challenges is the adoption of a data-driven approach-one that takes all stakeholders, students included, into account. The insights gleaned from the survey of students provide a valuable foundation for informed decision-making. By collecting data on students' perspectives, expectations, and usage patterns regarding AI tools, institutions can tailor their strategies to meet the needs of diverse student populations. Moreover, the longitudinal tracking of student opinions allows for the continuous refinement and adaptation of AI initiatives over time. In fact, 20.6% of the 2,749 respondents to an optional question suggested further use of surveys, questionnaires, and polls to continue collecting input from the campus community.

Promoting Responsible and Ethical Use

One of the key barriers to the adoption of AI among faculty is the perception of AI tools as potential liabilities, particularly in relation to plagiarism and academic misconduct. While the majority of students (71%) recognize the importance of AI in their future careers, many face barriers due to a palpable lack of encouragement from professors.

To overcome this challenge, institutions must provide educators with training on the beneficial and responsible uses of AI. Likewise, educators must provide students with clear guidelines and training on the ethical, safe, and effective uses of AI. Students are aware of the need for tool competence. The survey showed consistent references to students' desire for AI mastery both to be successful students and to be prepared for a career market that requires such skills. Responses included the following:

"There's a need for educational resources that demystify AI."

"Teachers should be equipped to integrate AI into their teaching."

"Training in AI should be part of our curriculum."

"AI literacy should be a focus for future professionals."

"AI will require us to continually adapt and learn new skills."

When AI use is reframed as a skill rather than a threat, faculty and students can be empowered to leverage AI tools to enhance teaching, research, and learning, thereby gaining a competitive advantage in the digital age.

In addition to affordability and access concerns, ethical considerations surrounding AI implementation loom large. The use of AI tools that are not approved by an institution poses significant risks to both students and institutions. For example, campus community members may unknowingly feed protected data into free or otherwise unvetted AI tools, exposing potentially confidential or sensitive data. To safeguard privacy and mitigate other risks, institutions must establish clear guidelines on the ethical, transparent, and safe use of AI. These guidelines should outline a procurement process that includes a review for acquiring AI tools, noting protocols for vetting and approving standard AI tools and for continuing with ongoing training and support to promote responsible AI practices.

Leveraging Students' Insights for Faculty Development

The Academic Senate of the California State University (CSU), the nation's largest four-year public university system, passed a resolution in 2023 calling for, among other items, "professional development opportunities for faculty to learn about generative AI and its applications to ensure they are prepared to effectively integrate it into their teaching." Further, diverse U.S. regulatory and governance agencies have called for widespread AI literacy training. For example, H.R. 6791 (Artificial Intelligence Literacy Act of 2023) was introduced in the U.S. House of Representatives in December 2023. In light of this call to action for comprehensive AI literacy, institutions must develop and implement structured training initiatives that encompass both technical proficiencies and pedagogical strategies pertinent to AI. These programs should offer a diverse array of practical and theoretical approaches, equipping faculty with the requisite skills to engage with AI effectively while instilling a thorough understanding of the ethical considerations required to guide students in responsible AI utilization.

At SDSU, one of the 23 CSU campuses, the Information Technology Division and the Office of the CIO collaborated with AI Faculty and Student Fellows to embrace a shared governance approach when designing both the AI student survey and the Academic Applications of AI (AAAI) microcredential program. The AAAI training aims to equip faculty with the practical, ethical, and interdisciplinary know-how necessary to leverage AI technologies across various academic domains. Delivered through the university's learning management system, the AAAI program comprises modules covering practical skills and theoretical foundations, offering participants a pathway to earn a digital badge on completion of all required activities.

This model of faculty and student-driven resource development, informed by insights from the AI student survey, exemplifies a commitment to ongoing learning and growth. By engaging students and faculty in the design, development, and facilitation of the microcredential, this inclusive approach not only created a sense of shared ownership but also galvanized institutional support for AI resources campus-wide. Furthermore, it facilitated the establishment of clear ethical guidelines governing AI use, fostering coherence in students' understanding of faculty members' expectations.

Currently, these guidelines are being considered by the SDSU University Senate, underscoring the impact of the AAAI program in shaping institutional policies and practices surrounding the use of AI in higher education. Importantly, these guidelines are just that: guidelines. By avoiding the impulse to issue firm directives or "policy" requirements, which tend to ossify in large organizations such as higher education institutions, the IT Committee of the SDSU University Senate left the door open for making easy updates when the AI ecosphere shifts again-as it surely will do, and soon. We also demonstrated a commitment to honoring academic freedom for our faculty stakeholders, increasing buy-in and follow-through on the front lines.

A Call to Action

As AI continues to reshape the landscape of higher education, the need for data-driven strategies has never been more pressing. By harnessing the power of students' insights, institutions can quantify structural disadvantages-for example, how some students will have easy access to a range of sophisticated and specialized AI tools while others will struggle to capitalize on free and less-powerful versions of the same AI. Gathering student data keeps students at the core of the decision-making process. Knowing your students is essential for bridging the AI divide and paving the way for a more inclusive and equitable future. Let us seize this opportunity to empower faculty, staff, and students in embracing the transformative potential of AI in higher education.

SDSU extends an invitation to higher education leaders to join us in this critical endeavor by leveraging our AI student survey and sharing data on the evolution of the AI student experience. The SDSU AI Student Survey Instrument is freely available for community members to use or adapt. Together, we can chart a course toward a future in which AI serves as a catalyst for innovation, equity, and excellence in higher education and the workforce.

Notes

1. The survey was sent to 37,728 undergraduate and graduate students, with a 20.7% (7,811) raw response rate. After data cleaning, our final sample size was 7,025. As far as we have found, this is the largest higher education student survey focusing exclusively on AI.

2. For more details on the survey results, see David Goldberg, Elisa Sobo, James Frazee, and Sean Hauze, "Generative AI in Higher Education: Insights from a Campus-Wide Student Survey at a Large Public University," in Proceedings of the Society for Information Technology and Teacher Education (SITE) 2024 (Association for the Advancement of Computing in Education, 2024, in press).

3. Adela de la Torre and James Frazee, "Bridging the AI Divide: A Call to Action," Inside Higher Ed, April 4, 2024.

4. Veronica Diaz, "Exploring the Opportunities and Challenges with Generative AI," EDUCAUSE Review, February 6, 2024.

5. EDUCAUSE, Council on Governmental Relations, and the Association of American Universities, Comments in response to FAR Case 2021-017, "Federal Acquisition Regulation: Cyber Threat and Incident Reporting and Information Sharing," February 2, 2024.

James P. Frazee is Interim Chief Information Officer and Vice President for the Information Technology Division at San Diego State University.

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using student data to bridge the ai divide

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Using Student Data to Bridge the AI Divide

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Knowing your students is essential for bridging the AI divide and paving the way for a more inclusive and equitable future.

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07242023 IDN Webinar Recording Presentation 2

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10 Ways Tech Leaders Can Step Up and In to the Generative AI Discussion in Higher Ed

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10 Ways Tech Leaders Can Step Up and In to the Generative AI Discussion in Higher Ed

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The use of generative AI tools on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and staff about how to navigate these new technological waters.

Credit: Nathapol Kongseang / Shutterstock.com © 2023

In April 2023, we were involved in a panel with students at College Unbound. The conversation-"Generative AI and Higher Education: Disruption, Opportunities, and Challenges" -offered many different highlights, and the students brought rich thoughts, provocative considerations, and smart ideas, reinforcing the fact that discussions around what to do about generative AI (or about anything else, for that matter) are enhanced when students are involved.

Toward the end of the panel conversation, Stan asked the students what they thought could be done to help faculty, students, and staff navigate the rise of AI. Essentially, he was curious to hear about the roles that technology and other leaders could fulfill. After thinking about their answers and engaging in further reflection, we came up with ten suggestions for how to step up and in to the generative AI discussion in higher education.

1. Offer Short Primers on Generative AI

Short primers offered on the topic of generative AI would ideally be text and video and could be harvested from what is already available on different websites and video platforms (e.g., YouTube). LinkedIn Learning would be another useful place to find content if your institution has access. The goal here is to provide short but clear content, ideally focused on the following questions:

What is generative AI?

Why does generative AI feel new and/or different (in general and in higher education, specifically)?

Who are the current providers of generative AI tools?

How should these tools be used?

What is the role of prompting in better understanding the usefulness and limitations of generative AI, and what are some strategies for using prompting?

Pro Tip: Use ChatGPT or other generative AI tools, and record what you do. This recording can serve as the basis for your own videos as an example of how you've integrated generative AI.

2. Explain How to Get Started

Providing clear guidance for faculty about how to get started using generative AI and what to try first can be helpful. Maybe the guidance is a "top 10 things to try," with each suggestion becoming progressively more dynamic or complex. This is your opportunity to help faculty and staff see the power of generative AI in action. Consider providing prompts that can help them understand how this tool would be useful for them individually and in relation to their roles at the institution, including for students.

3. Suggest Best Practices for Engaging with Generative AI

The next step is to target those who may already be familiar with generative AI tools but need help improving their skills. Guidance here might include a "Prompt Cookbook" with up/down voting or feedback from users who have found these tools useful/helpful or who want to add recommendations. These best practices might also identify limitations of the tools, along with workarounds. Linking to resources for the proper citation of generative AI tools would also be helpful.

4. Give Recommendations for Different Groups

This step might be tricky to determine and appropriately represent the different groups, but the goal is to highlight several different groups of people who are navigating generative AI on campus and provide them with some insight. For instance, there are many skeptics about these tools (and given what we know about AI, these skeptics will be important voices to include), and there are many people who are excited about the tools. There are also people who don't even know how they feel about generative AI. A resource can provide a mixture of considerations for each type of person. For the skeptic, a framework could validate that skepticism while also providing some considerations about the importance of learning more about the generative AI tools that many industries will be using soon or are already using. For the tech evangelist, a framework could provide a healthy dose of skepticism and concern about these tools, particularly around biases, the environmental impact, and human exploitation related to the creation and maintenance of generative AI. Finally, for the person who hasn't formed any opinion, the framework could provide an anchor with which to begin exploring and testing out generative AI tools with a bit of clarity and confidence.

The overall goal isn't to persuade people to feel better about whatever their stance is but, rather, to help all of them leverage a more balanced and informed perspective.

5. Recommend Tools

Every day, it seems like a dozen new AI tools are being promised, previewed, or released. Many on campus are a bit lost, and this could be a great opportunity for technology and other teams to provide some recommendations for some of the best AI tools to use. These teams could also suggest relevant considerations: What do the generative AI tools cost? What do we know about data privacy and these tools? What should/shouldn't the tools be used for? Is there an institutional account/discount for any of these tools? New options include anticipated announcements and beta deployments from major productivity suite producers such as Microsoft and Google. Generative AI search is now available on both platforms, and productivity suite enhancements are being added every day.

6. Explain the Closed vs. Open-Source Divide

As open-source versions gain steam, tracing out the differences and values among them can be invaluable for users on campus. The open-source models are placing capable tools in the hands of hobbyist and professional organizations alike. This will lead to a proliferation of finely tuned AI models contextual to specific institutions. Colleges and universities will increasingly begin to host their own large language models. This will provide options for localized training and utilization with less risk and more utility due to potential personas and organizational data access.

Currently, most of these open-source models come with a performance penalty and do not measure up to the corporate models. However, who knows how long this advantage will remain intact? Engineers at Google, for example, are famously rumored to have stated that they have no "secret sauce" and no real competitive moat between other providers and open-source models.

Priming people on your campus about the differences between the models could help them be ready to embrace (or at least understand) the importance of whichever model your institution may ultimately choose. This could also help institutional leaders navigate issues such as how to allow access to the large language models for training purposes, how to decide who should maintain custody and update the derivative datasets, and how to determine what should and shouldn't be shared in the interest of academic openness and freedom of expression.

7. Avoid Pitfalls

While the use of generative AI tools is skyrocketing, this is often being done without organizational contracts or formal risk analysis. Institutional technology and other leaders need to understand the risks and opportunities associated with new AI technologies. Offices of general counsel will want to review the term sheets for the "free to use" options and to negotiate favorable terms for the for-fee and site licenses their institutions commit to. CIOs and CISOs should weigh in on what is acceptable use for their institutions. Clear written guidance needs to be quickly developed and socialized. When introducing these technologies to members of your campus community, be sure to advise them to steer clear of placing sensitive or restricted data into the tools and to limit identifiable information.

8. Conduct Workshops and Events

Holding workshops, drop-in sessions, and campus events can be another means of bringing people together to help them understand the complexity of generative AI. These trainings might be more effectively targeted toward different populations (e.g., faculty, staff, students) and different levels of users (e.g., novice, intermediate, and expert). Additionally, this could be a great way to recruit people for working groups or communities of practice as the generative AI landscape continues to change.

9. Spot the Fake

Providing tips about how to avoid fake or lower-quality tools could also be extremely helpful. Many quickly emerging AI platforms offer very little privacy information or even pricing (sometimes being framed as in "beta" mode) but will ask people for access to private data (e.g., personal information, email, data). Where and when should this be considered? Additionally, with all the hype currently surrounding AI, many of these tools and platforms will not pan out as viable in the long run. Experimentation is encouraged, but institutional technology and other leaders should engage with their eyes wide open and take measured risks when investing funds into capability exploration. Think back to the early days of the internet and the numerous websites that went the way of the dinosaur in the dot-com bust.

10. Provide Proper Guidance on the Limitations of AI Detectors

Many people and companies are likely becoming rich right now by creating tools that detects students' work created with generative AI. The reality is that these tools are not going to work in any significant or long-term way. There are too many other tools that can trick AI-detection tools. There is also increasing evidence that AI plagiarism detection not only creates false positives but does so disproportionately for students for whom English is not their primary language. A primer that explains the underlying problems and realistic abilities of AI-detection tools could save colleges and universities a lot of money.

The institutional IT organization is often the hub of digital safety, security, and privacy considerations. All of those manifest when it comes to the use of generative AI tools. This usage on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and staff about how to navigate these new technological waters. If this can be done with good resources, accessible language, and thoughtful support, these leaders can provide a life raft to many.

Notes

1. For example, see this "Prompt Guide" provided by Lance Eaton to attendees at the session "Ready or Not, Here AI Come: Exploring the Role of Generative AI in Higher Education," Massachusetts Colleges Online Conference on eLearning, June 15, 2023.

2. See, for example, "How Do I Cite Generative AI in MLA Style?" MLA Style Center (website), accessed September 20, 2023; Timothy McAdoo, "How to Cite ChatGPT," APA Style Blog, April 7, 2023; "Citation, Documentation of Sources," Chicago Manual of Style Online (website), accessed September 20, 2023.

3. Tara García Mathewson, "AI Detection Tools Falsely Accuse International Students of Cheating," The Markup, August 14, 2023.

Lance Eaton is Director of Digital Pedagogy at College Unbound in Providence, Rhode Island.

Stan Waddell is Vice President and CIO of Carnegie Mellon University.

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10 ways technology leaders can step up and in to the generative ai discussion in higher ed

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10 Ways Technology Leaders Can Step Up and In to the Generative AI Discussion in Higher Ed

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The use of generative AI tools on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and st

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Cheating or Learning? Walking the AI tightrope in education

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Cheating or Learning? Walking the AI tightrope in education

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Cheating or Learning? Walking the AI tightrope in education | Erik Winerö | TEDxGöteborg

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Professors Cautious of Tools to Detect AI-Generated Writing

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Professors Cautious of Tools to Detect AI-Generated Writing

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Inside Higher Ed | Higher Education News, Events and Jobs

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Professors proceed with caution using AI-detection tools

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Mixed performance by AI-detector tools leaves academics with no clear answers.

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What ChatGPT’s Voice and Image Capabilities Mean for Educators

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What ChatGPT’s Voice and Image Capabilities Mean for Educators

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Preparing for the Unanticipated: AI Applications in Higher Education

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Preparing for the Unanticipated: AI Applications in Higher Education

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Inside Higher Ed | Higher Education News, Events and Jobs

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Prepping for the unanticipated: AI applications in higher ed

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Generative AI (GenAI) has emerged and is developing far more rapidly than expected. How should universities prepare for the impact that may not be anticipated?

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Will Our Educational System Keep Pace with AI? A Student's Perspective on AI and Learning

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Will Our Educational System Keep Pace with AI? A Student's Perspective on AI and Learning

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Reflecting on AI and learning, a student offers four insights gleaned from firsthand interactions with ChatGPT.

Credit: VH-studio / Shutterstock.com © 2024

"Education is not the filling of a pail, but the lighting of a fire."

This quote, often attributed to the poet and Nobel laureate William Butler Yeats, captures the essence of education. A timeless goal is to light a fire within the hearts and minds of learners. Today, a groundbreaking invention stands ready to fuel the fire and revolutionize the educational landscape forever: AI chatbots.

The release of large language model (LLM) interfaces such as ChatGPT and Bard has sparked intense debates about their integration into and implications within educational institutions. Critics have raised valid concerns regarding academic dishonesty, as the remarkable capabilities of AI chatbots to produce human-like responses and intricate computer codes can be exploited for difficult-to-detect cheating in academia. Consequently, Seattle and New York City Public Schools, along with the Los Angeles Unified School District, swiftly imposed bans on AI chatbots on their campuses. The controversy surrounding AI chatbots also extends to the propagation of misleading information. Users in scientific and legal fields have encountered instances where AI chatbots generate inaccurate and misleading content. If left unmonitored and uncurated, AI-generated material may lead to the inadvertent spread of misinformation, particularly among students. These legitimate concerns provide further support for imposing restrictions on the utilization of AI chatbots in educational settings.

Ever since I was in middle school, I have been self-studying neurological diseases every summer. Initially amazed by the complexity of the human central nervous system (CNS), I gradually built a deep interest in the early diagnosis and innovative treatments of CNS disorders. I read books and articles on these diseases. This past summer, however, ChatGPT became an invaluable tutor and assistant for me (and for free!).

In the summer of 2023, my self-study focused on developing an innovative approach for early detection of body signals (also known as biomarkers) for brain tumors. My engagement with ChatGPT started with the prompt "What are the known biomarkers for CNS cancers?" For this type of question, I usually would have spent hours searching the PubMed database and reading review articles to find the answer. Frequently, I would encounter complex concepts and technical jargon that necessitated additional search and study, a cycle that tends to be both time-assuming and often unproductive. However, in the snap of a finger, ChatGPT listed eight categories of biomarkers, each with clear and concise explanations. I was eager to delve deeper into each of them with ChatGPT's assistance. During my subsequent study, one of the ChatGPT responses particularly piqued my interest: "The oncometabolite 2-hydroxyglutarate (2-HG) produced by the mutant isocitrate dehydrogenase (IDH) is a metabolic biomarker for gliomas. IDH-mutated gliomas are generally associated with a better prognosis than their IDH-wildtype counterparts." A series of questions began to surface in my mind: "How exactly is 2-HG formed?" "How does 2-HG affect the cancer itself?" "Why does the IDH mutation produce an oncometabolite but seem to be also favorable for glioma patients?" "Is ChatGPT wrong?" Intrigued, I carefully read several review articles and learned that 2-HG is an oncometabolite because it supports the survival and spread of cancer cells but that IDH mutations, which generate 2-HG, predict a better prognosis.

These paradoxical facts led me to further study the effects of IDH mutations in glioma cancer cells. My reasoning was simple: since IDH mutations cause detrimental effects by generating an oncometabolite, the very same mutations must also have beneficial effects to lead to a better prognosis. I was curious about these beneficial effects. Before the advent of ChatGPT, I assumed that only experts in this field could answer such a question. In the past, if I didn't encounter a simple article that explained the topic, my journey would have ended there. But thanks to ChatGPT, a single prompt was all that lay between me and the answer: "What effects of IDH mutations are detrimental for glioma cancer cells?" Again, ChatGPT showed me its superb capability in collecting and summarizing information: "IDH mutations can lead to a reduction in the production of NADPH, an essential molecule for handling oxidative stress, making the cancer cells more vulnerable to damage from reactive oxygen species." Aha! For a moment, I felt as though I were sitting beside a knowledgeable professor who understands all my naive inquiries and has unlimited time to explain answers to me in straightforward language. ChatGPT was all that I, as a young researcher, could ever ask for.

Yet though ChatGPT is an exceptional tutor with an encyclopedic wealth of knowledge, I consider myself to possess a unique advantage in terms of ingenuity. How so? Knowing that IDH mutations can have both good and bad effects on cancer patients, I asked ChatGPT: "What would you suggest to treat brain cancer with IDH mutations?" ChatGPT outlined only two areas of research, leaving me somewhat disappointed. The two were IDH mutant inhibitors and gene therapy, both of which aim to reverse all effects of IDH mutations, no matter whether they are good or bad. This meant that although 2-HG would no longer be produced, the cancer would also have a better chance of surviving. The fact that ChatGPT highlighted only these two options meant that there wasn't much research in the area, making me excited that I could possibly contribute. An idea began to form in my mind: "Could we not exploit the advantageous activity of IDH mutants to weaken cancer cells, while selectively eliminating the detrimental effect by converting the oncometabolite 2-HG into other, potentially beneficial, metabolites?"

As a result of this outcome, I am reflecting on how my experiences could impact my fellow students and others in the future. Here are four insights I have gleaned from my firsthand interactions with ChatGPT.

1. Any technology can be both beneficial and harmful. The outcome depends on how we use it.

Historically, the advent of transformative technologies-personal computers, the internet, Google, and Wikipedia, for example-has often been met with skepticism and concern at first. Critics have questioned the impact of these technologies on education, warning of potential increased academic dishonesty and diminished learning capabilities. However, over time, students have harnessed these very technologies as powerful learning tools. Across the United States, high schools are implementing one-to-one computing programs-initiatives that equip each student with a device, empowering them to conduct research online, foster their independent learning, and collaborate more effectively with peers. AI technologies can be seen in the same light.

However, the benefits or detriments of AI, like those of any other tool, depend largely on its usage. In the educational system, AI technologies can be utilized effectively to supplement classroom learning and make vast amounts of information readily accessible to students. This would improve learning outcomes and promote a more engaging educational experience. But if misused or relied on too heavily, AI technologies could also harm students' critical thinking and problem-solving capabilities. This highlights how important it is for teachers to craft questions and assignments that not only test knowledge but also stimulate deeper thinking and analysis. The questions and assignments should encourage students to analyze information and data, make connections between knowledge points, solve complex problems, and articulate their reasoning. These skills are needed both in the scientific community and in society. Knowledge is always at hand, but critical thinking skills can be gained only at the crucial learning stages in high school and college.

Another problem is that AI doesn't possess genuine emotions or the ability to truly understand and respond to human feelings. The use of AI could diminish a significant portion of human interaction in the learning process, resulting in a loss of social skills and interpersonal development. To solve this, teachers should allocate more effort to identifying and responding to the emotional needs of their students. They can provide the support, encouragement, and guidance that will motivate students in a way that AI cannot.

2. AI is changing the way we access information. How will the educational system adapt?

Our current educational system is in dire need of an adjustment to reflect transformative innovations. In the past, individuals were required to seek out knowledge, since access to it was limited. This process usually involved rote memorization, sometimes at the expense of a deep understanding of the knowledge. Today, technologies have made this process unnecessary in most cases. But despite the significant societal and technological advances of recent decades, our educational system has remained largely unchanged at its core. Conventional teaching still promotes a tendency among students to cram information from textbooks for mere regurgitation during tests. The emphasis of learning is on correctness and precision, not creativity or analysis.

AI technologies will also create more jobs, such as AI specialists, data analysts, and software engineers. Roles that require human traits such as creativity, critical thinking, empathy, and complex problem-solving skills will remain in high demand. Furthermore, new professional classes are on the horizon, including strategic decision-makers, innovative thinkers, AI ethicists, and AI trainers. These roles will be pivotal in navigating the ethical, social, and strategic challenges of AI applications.

With these roles in mind, educational leaders at the high school and college levels should prioritize the development of the essential skills in which humans excel and outshine AI.

The wealth of information collected by AI can be harnessed in tandem with the creativity cultivated within the educational system, resulting in a potent hybrid intelligence system. These dynamic interactions between humans and AI would foster the generation of unique ideas and breakthroughs. My experiences last summer showcase how learners, including high school students like myself, can comprehend challenging subjects and generate new ideas in ways previously unimaginable.

Similarly, greater incentives should be given to crowdsourcing platforms that value and reward public contributions of human intelligence aimed at addressing complex problems. In this evolving landscape, AI should be designed to continuously update and integrate newfound human insights, creating new strata of knowledge. This hybrid intelligence system necessitates human competence in AI interaction. Consequently, future curricula should encompass human-AI interaction and offer classes covering subjects such as prompt engineering to enhance students' information-retrieval skills and help them achieve more accurate results.

By integrating these strategies into our educational system, we can foster an environment in which students are not just passive learners but instead are actively engaged with AI as creative thinkers. This will encourage students to formulate their own ideas and hypotheses, thereby nurturing a sense of intellectual ownership and growth. By this means, education not only prepares students for tests but also empowers them to make substantive contributions to the community while in school.

3. Personal interests and curiosity are the driving forces of learning. Are we ready to fully step into personalized education?

We've all experienced the thrill of delving into a subject we find fascinating or discovering something new in a field we're passionate about. We've also endured those dreary hours spent on uninteresting tasks. Clearly, our brains do not function at the same level in these contrasting scenarios. When routine, repetitive tasks are automated through AI, the unique interests, talents, and characters of individuals will become increasingly valuable.

In the years ahead, personalized education will play a crucial role in maximizing students' potential and addressing diverse learning needs. With advancements in technology and data analysis, education can be tailored to the unique strengths, interests, and learning styles of each student. Tailored learning paths can allow students to progress at their own pace and delve deeper into their areas of passion. Adaptive learning platforms, powered by AI, can provide real-time feedback and assessments, enabling educators to identify areas of improvement and customize instruction accordingly. For example, Duolingo, an app for learning languages, uses AI to identify words and phrases a user might be struggling with; it then curates a special lesson focusing on these words. By embracing personalized education, we can unlock the full potential of every learner, nurturing a generation of individuals who are prepared to thrive in the complexities of the future. In doing so, however, we must move beyond academics to also instill a lifelong love of learning and help students cultivate ethical values, social responsibility, and global citizenship. This allows students to become more active participants in their own future and contributors to their community.

4. AI-assisted personalized learning will promote equality in education.

Students, regardless of their backgrounds, possess abundant creativity and originality within their individual passions. AI has the potential to uplift ingenious ideas and showcase brilliant minds that may have been overlooked in the past. Personalized education holds tremendous potential as a vehicle to address the educational disparities and inequalities disproportionately affecting underrepresented minorities. By tailoring instruction to the specific needs, backgrounds, and cultural contexts of students, personalized education can provide a more inclusive and equitable learning experience. It allows educators to recognize and appreciate the unique strengths and perspectives that underrepresented minority students bring to the table.

AI-assisted personalized education also offers an effective solution to help bridge the achievement gap. By providing targeted and precise support, resources, and interventions tailored to individual learning challenges, AI can significantly promote academic success by assessing learning patterns, strengths, and weaknesses in real time, offering personalized strategies and resources that promote successful learning experiences. This level of customization can prove invaluable in addressing educational disparities and promoting academic success among underrepresented students.

*****

AI chatbots and other AI technologies possess the transformative power to revolutionize the learning process and reshape our world. Progress has always been about how societies use technology. The integration of the hybrid intelligence system into our society can light a fire within the hearts and minds of learners of all ages. Those who adapt to and embrace the remarkable capabilities of AI will hold the key to the door to the future.

Notes

1. Gary Marcus, "AI Platforms like ChatGPT Are Easy to Use but Also Potentially Dangerous," Scientific American, December 19, 2022; Lyle Moran, "Lawyer Cites Fake Cases Generated by ChatGPT in Legal Brief," Legal Dive, May 30, 2023.

2. "Exploring Teacher and Student Attitudes toward Chatgpt," Walton Family Foundation (website), May 4, 2023.

3. Christine Persaud, "Learning Styles: The Ultimate Guide," Top Hat blog, February 4, 2019.

4. The White House, "Fact Sheet: Biden-Harris Administration Announces over $40 Billion to Connect Everyone in America to Affordable, Reliable, High-Speed Internet," June 26, 2023.

William J. Yin, a talented and popular student at Oconee County High School, Watkinsville, Georgia, was known for his academic achievements, athletic prowess, musical talents, and his tireless support for his peers.

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will our educational system keep pace with ai? a student's perspective on ai and learning

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Will Our Educational System Keep Pace with AI? A Student's Perspective on AI and Learning

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Reflecting on AI and learning, a student offers four insights gleaned from firsthand interactions with ChatGPT.

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Integrating Generative AI into Higher Education: Considerations

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Integrating Generative AI into Higher Education: Considerations

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Integrating Generative AI into Higher Education: Considerations

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Integrating AI into higher education is not a futuristic vision but an inevitability. Colleges and universities must adapt and prepare students, facul

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How (and Why) the University of Michigan Built Its Own Closed Generative AI Tools

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How (and Why) the University of Michigan Built Its Own Closed Generative AI Tools

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To address privacy, accessibility, and affordability issues, the University of Michigan decided to develop closed generative AI tools for use by its campus community.

Credit: Muslianshah Masrie / Shutterstock.com © 2024

Institutional Profile

The University of Michigan (U-M) is a public research university with campuses in Ann Arbor, Dearborn, and Flint, as well as a medical center in Ann Arbor. Founded in 1817, the university is the oldest in Michigan and a founding member of the Association of American Universities. U-M offers more than 250 degree programs through 19 schools and colleges. In fall 2023, the enrollment was 52,065, including 33,730 undergraduates.

The Challenge/Opportunity

Informed by the recent findings of a campus committee studying generative artificial intelligence (AI), the IT leadership at University of Michigan confronted an interesting tension in July 2023.

On the one hand, the exciting tool of the moment - ChatGPT from the San Francisco startup OpenAI - raised a host of privacy, accessibility, and affordability questions that a public institution of higher education has to account for. For example, data from queries on ChatGPT is shared back with OpenAI. Additionally, the product wasn't fully compliant with the web accessibility technical guidelines U-M uses, which meant it wasn't ideal for the screen reader technology that many students rely on.

On the other hand, everyone in the IT leadership agreed that a major research university needed to make the latest AI tools available to all those who will inevitably want to explore it: researchers, instructors, student support professionals, operational staff, and students.

"Our vision at the University of Michigan is to be a leader when it comes to generative AI," said Ravi Pendse, U-M's vice president for information technology and chief information officer. "Not only for our peers, but also for the rest of the world. We want to show how you can provide fair, responsible, and forward-thinking access to this kind of technology for an entire community."

The U-M Generative Artificial Intelligence Advisory Committee - convened by the U-M provost and Pendse and comprising 20 faculty, students, and staff members from various disciplines - also outlined how ChatGPT is offered in more and less powerful versions at different costs, which unfortunately had the potential to further inequality among students.

The committee released its report on June 30, 2023, detailing the concerns about privacy, accessibility, and affordability, among other issues. One of the recommendations was to "create, accommodate & deliver" - "as soon as feasible" - basic consumer and advanced research generative AI tools that are secure and equitably accessible.

Figure 1. Summary of Overarching Findings

Credit: Generative Artificial Intelligence Advisory Committee Report. Reprinted with permission.

As Pendse said: "The report was a great start, but we did not want to end our efforts there. That is not the Michigan way. Michigan leads. That is when we decided that we needed a sandbox of our own to experiment with these AI tools."

Pendse and U-M's Information and Technology Services (ITS) set out to develop custom AI tools for the U-M community to use in lieu of emerging public products from OpenAI and others. "There are a lot of people playing in this space, but we didn't see anyone marching forward with inclusion, equitability, and accessibility at the forefront," said Robert Jones, ITS executive director of emerging technology and support services. "That's how we thought we could make a difference."

They envisioned a suite of tools to serve the broad and in-depth needs of a large research university:

A Q&A tool similar to ChatGPT

A no-code platform that allows different units around campus to develop their own AI-based tools for specific contexts

A toolkit with which developers and researchers can build, train, and host their own AI models and environments

They knew a key part of their approach would be "closed AI." Instead of queries being used to continually train and tune the AI, information would be kept private, and the language model the U-M tools relied on would be updated and tuned by ITS staff.

As the advisory committee's report also noted, the challenges that generative AI presented were imminent, since the community was already using OpenAI products. U-M leaders decided that they wanted students, faculty, and researchers who would be arriving on campus in fall 2023 to find a set of generative AI tools waiting for them with three advantages: utilized closed AI methods that protect privacy, were free to use, and had accessible design. They gave themselves a deadline of August 21 to build the tools.

The Process

The project was unique, said Jones, since it brought together both vendor resources and internal software development to build custom AI services that met the specific needs of the U-M community.

Members of the ITS Emerging Technology team worked through the summer to create and test U-M's AI tools. They achieved this in part by licensing from Microsoft to use its large language model within a walled-off Michigan environment so that queries made by their users are not shared with OpenAI.

This allowed them to deploy other language models, including any developed at Michigan. "You'll notice I didn't say 'large language,'" Pendse said. "If you have verified data, you can get equally accurate results with a much smaller model."

The team was made up of about a dozen people, including two front-end developers, a full-stack developer, and several other supporting roles. "The other executive directors in my organization were incredibly supportive and generous," Jones said of the ambitious timeline. "The senior leadership established an outstanding team. There were a lot of unknowns about whether we could even do what we wanted to do. It was a big idea with no precedent, but there was commitment. If there was a question without an answer, we were going to figure it out."

Outcomes and Lessons Learned

On August 21, U-M did have three unique generative AI tools ready for returning students and employees.

1. U-M GPT is the tool that most resembles ChatGPT. It is able to answer questions, produce written content, and make recommendations. Additionally, U-M GPT supports multiple commercial and open-source language models and AI art generators, broadening its utility and applications. U-M GPT was also designed to work seamlessly with screen readers, making it more accessible than ChatGPT. U-M GPT is free for all members of the U-M community.

2. U-M Maizey is a no-code platform that allows users to build unique and customized chat programs by using their own datasets in combination with U-M's AI language models. Users design context-specific AI experiences that they then share with their audience in the U-M community. Individual departments, units, or projects pay for U-M Maizey through the university's departmental charge system.

3. U-M GPT Toolkit is designed for AI developers who require full control over the AI model and environment that they are building, training, and hosting. Researchers and developers who want to use the U-M GPT Toolkit must contact the ITS AI team for access.

All three tools are currently approved for "moderate sensitive" data. This means they can be used with information covered under the Family Educational Rights and Privacy Act, but they cannot yet be used with highly sensitive data, such as protected health information.

The Response from the U-M Community

U-M's AI tools currently have an average of 15,000 users a day, including those who are using it as part of coursework. For example, English instructors have asked students to use U-M GPT to create the first draft of an essay and then to revise the draft in class.

U-M Maizey-the no-code platform for creating unique chat tools-has already been used in a wide range of projects:

Student advising in the College of Literature, Science, and the Arts is creating an AI academic advisor to handle frequently asked questions.

Procurement is creating an AI-enabled platform that will help users easily complete requests for proposals.

ITS built a chatbot for Student Life to help students explore more than 1,500 student organizations at U-M.

The U-M Library is creating a natural-language book-recommendation service. U-M students will be able to ask questions such as "What are some of the best books in the library about early Netherlandish paintings?" or "What books will teach me about social epidemiology?"

Two Ross School of Business faculty members, Andrew Wu and Jun Li, built an AI tutor for their courses. Informed by past course data, it is currently serving more than 1,000 students. The AI tutor was tested on qualitative and quantitative questions and was found to have answered questions 94 percent as effectively as, or better than, those teaching the course. During the testing, the AI tutor was also found to have outperformed ChatGPT 24 percent of the time.

Meanwhile, U-M GPT Toolkit is being used for experiments in clinical settings to optimize patient notes and to translate prescription notes to ensure that patients are offered generic drug options when appropriate.

Advice for Other Organizations

Since the launch of U-M's suite of closed AI tools, more than 49 institutions have gotten in touch with U-M to ask for advice about creating their own. U-M is sharing its experience with those institutions and in the future may even share its IT talent with smaller institutions, such as community colleges, to help them develop their own AI.

In the meantime. U-M is continuing to build and refine its tools. Next year, U-M students may find themselves with a personal AI assistant, for example. The university is also planning to improve the AI so that it can be used with sensitive medical information in the future, allowing the university's academic medical center to benefit more from the tools.

Moving into the Future with AI

In some senses, AI has been around for decades for developers and researchers. But generative AI is a new phase, and public tools like ChatGPT have democratized access to it. Like the web browser, which made the internet available to non-coders, generative AI has opened the possibilities of AI to everyone, no matter their technological skill level. This has come with its share of fears about automation and job loss.

"I empathize with those concerns," said Pendse. "We have to approach AI thoughtfully and carefully. But I firmly believe that the true value of GenAI is in its potential to enhance and augment humanity, not replace it. AI will not take jobs away from you. But people who know how to use AI might." He believes U-M's new tools will give its students not only the ability to move into a world that has been changed by ChatGPT but also the skills to use that technology responsibly and well.

Note

Karthik Duraisamy, Generative Artificial Intelligence Advisory Committee Report, University of Michigan, June 30, 2023.

Case Study Contributors: Ravi Pendse, Vice President for Information Technology and Chief Information Officer, University of Michigan; Robert Jones, Executive Director of Emerging Technology and Support Services for Information and Technology Services (ITS), University of Michigan

This case study is one in a series being produced by EDUCAUSE Communities and Research. A. J. O'Connell is a writer with McGuire Editorial & Consulting specializing in education technology.

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How (and Why) the University of Michigan Built Its Own Closed Generative AI Tools

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To address privacy, accessibility, and affordability issues, the University of Michigan decided to develop closed generative AI tools for use by its c

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Ethical Implications of ChatGPT in the Educational Setting

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Ethical Implications of ChatGPT in the Educational Setting

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Ethical Implications of ChatGPT in the Educational Setting

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OpenAi’s ChatGPT is the most advanced chatbot that can take an inquiry and produce a very readable and impressive summary

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Using AI to Design Better Online Learning

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Using AI to Design Better Online Learning

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Luke Hobson

Dr. Luke Hobson is the Senior Instructional Designer and Program Manager at MIT xPRO and a Lecturer in the Department of Teaching and Learning at the University of Miami's School of Education and Human Development. He's also the author of the book What I Wish I Knew Before Becoming an Instructional Designer, the host of the Dr. Luke Hobson Podcast and YouTube Channel, and the Founder of Instructional Design Institute. Dr. Hobson was named as one of the top learning influencers in 2022 and top e-learning experts in 2023.

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Using AI to Design Better Online Learning

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Join us as we explore the practical applications of AI for educators and instructional designers, with real-world examples of how you can use tools like ChatGPT, Google Bard and DALL-E to design better online learning experiences.

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Educause has also collected a series of carefully curated articles that educate folks on AI in the classroom.

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This report describes an ideal future for generative AI in higher education, drawing on the insights of a small panel of teaching and learning higher education experts, focusing on a 10-year timescale.

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use of artificial intelligence in higher education

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impact of artificial intelligence on teaching and learning in higher education

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This page provides resources regarding the use of AI in higher education.

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15 Ways to Use ChatGPT as an Instructional Designer, Instructor, and Teacher

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15 Ways to Use ChatGPT as an Instructional Designer, Instructor, and Teacher

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15 Ways to Use ChatGPT as an Instructional Designer, Instructor, and Teacher

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10 Ways Artificial Intelligence I﻿s Transforming Instructional Design

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10 Ways Artificial Intelligence I﻿s Transforming Instructional Design

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Artificial intelligence (AI) is providing instructors and course designers with an incredible array of new tools and techniques to improve the course design and development process. However, the intersection of AI and content creation is not new.

Credit: 3rdtimeluckystudio / Shutterstock.com © 2023

Artificial intelligence (AI) is rapidly shaping the teaching and learning experience by providing instructors and course designers with an incredible array of new tools, capabilities, and techniques to improve the course design and development process. However, the intersection of AI and content creation is not new. For several years, tools have emerged to support automatic speech recognition (ASR); image-description generation; plagiarism detection; assessment and grading; mathematical expression interpretation; graphing and mind mapping; and improved spelling, grammar, and writing. Generally, these tools include valuable time-saving and content-validation features. They are often third-party tools used to satisfy a specific course task or expedite a process. It is only a matter of time before these technologies become omnipresent in nearly every layer of course development and embedded in every product course developers employ.

What does this mean for the field of instructional and course design? I have been telling my graduate instructional design students that AI technology is not likely to replace them any time soon because learning and instruction are still highly personalized and humanistic experiences. However, as these students embark on their careers, they will need to understand how to appropriately identify, select, and utilize AI when developing course content. Examples abound of how instructional designers are experimenting with AI to generate and align student learning outcomes with highly individualized course activities and assessments. Instructional designers are also using AI technology to create and continuously adapt the custom code and power scripts embedded into the learning management system to execute specific learning activities. Other useful examples include scripting and editing videos and podcasts.

Here are a few interesting examples of how AI is shaping and influencing instructional design. Some of the tools and resources can be used to satisfy a variety of course design activities, while others are very specific.

1. Personalized Learning

AI can help instructors create learning experiences that are tailored to each student's individual needs, preferences, interests, and skill level. Some of these adaptive products have been widely available for years, but recent technological advances can now be leveraged to provide even more personalized instructional opportunities for learners and course designers. Instruction can be customized using AI-generated personalized learning to fit specific learning requirements (goals, skills, career paths, etc.). AI-based learning systems allow instructors to access useful information about learners' abilities and progress and provide suggestions for how to customize instructional methods to meet each student's specific needs.

AI-Generated Personalized Learning Example

Consider an English language course that uses AI-generated personalized learning. The AI platform uses reading, writing, and comprehension skills data to identify a learner's areas of proficiency and improficiency. The platform uses assessment data to tailor the course content to fit the student's needs. For example, if a student shows proficiency in reading comprehension but struggles with grammar, the system will assign more exercises focused on improving grammar. The content could be a mix of text-based lessons, interactive activities, and videos or audio recordings. As the learner progresses through the course, the technology monitors the student's progress and adjusts the course material in real time. If a learner demonstrates an understanding of certain grammar concepts, the system will introduce more complex topics or switch focus to another area. The platform provides instant feedback on assignments and exercises to help keep learners engaged and motivated. AI platforms might soon be able to provide encouraging messages when a learner achieves certain milestones, further promoting engagement.

2. Adaptive Learning

Adaptive learning is one technique for providing personalized learning. It is one of several instructional approaches that include competency-based learning and differentiated instruction. AI-powered adaptive learning systems can adjust the pace and difficulty of the course material based on the learner's performance. Adaptive learning has been shown to enhance and improve retention, boost learner engagement, reduce training time, and provide rich analytics that track the learner's performance over time. Imagine a course that includes AI-generated activities that can responsively adapt and build new experiential pathways as a learner interacts with it. These are not predefined proficiency-based pathways that prescriptively redirect the learner but rather generative pathways that are created dynamically using a combination of the learner's responses and time on task, the difficulty level of the activity, and other data.

AI-Generated Adaptive Learning Example

Consider an algebra course that uses AI-generated adaptive learning. The system uses the results of a diagnostic assessment to establish a baseline of the student's abilities and identify knowledge gaps. For a learner who demonstrates proficiency in basic operations but struggles with factoring polynomials, the AI technology creates a personalized learning pathway beginning with content and exercises that address the identified weaknesses. For example, the AI system might first offer lessons and practice exercises on factoring polynomials. The system continually tracks performance as the student progresses through the course. If the learner answers factoring questions correctly, the system introduces more complex concepts, such as quadratic equations. In a hybrid learning environment, the AI can also provide instructors with data about each learner's performance and progress, including which learners are at risk of falling behind, which topics are causing the most difficulty, and other insights to help instructors tailor their teaching.

3. Intelligent Tutoring Systems

An AI-based intelligent tutoring system (ITS) can provide learners with personalized guidance and feedback to help them master specific concepts or skills. For a particularly difficult subject, an AI-based tutoring system might include foundational information and then offer examples that are deconstructed for the learner as needed. An ITS can provide a human-like learning experience, stimulate self-learning at appropriate points, and offer multiple modes of personalized feedback. One might think of products like ChatGPT as intelligent tutors, but unlike AI-driven natural language processing tools, an ITS can be simultaneously tailored to each learner's preferences and deeply embedded into the learning experience.

ITS Example

Consider a physics course that uses an AI-based ITS. When a learner first interacts with the ITS, the system performs an initial assessment to determine the student's current understanding of physics. This assessment could involve asking the learner to solve a range of problems that cover different aspects of physics. The ITS analyzes the assessment data to identify knowledge gaps. Perhaps the student demonstrates proficiency in Newton's laws but struggles with understanding electric fields. The ITS tailors a learning pathway to address the learner's needs. In this case, it might focus on lessons and problems related to electric fields. The ITS offers interactive problem-solving exercises and provides immediate feedback. If the learner makes a mistake, the system explains why the given solution is incorrect and guides the student through the correct problem-solving process. The system might also offer hints or strategies for approaching similar problems. The ITS monitors the student's progress throughout the course. If the learner shows significant improvement in understanding electric fields, the ITS adapts and introduces more complex problems or new concepts.

4. Natural Language Processing

Closely related to intelligent tutoring systems, AI-powered natural language processing (NLP) technology enables conversational interfaces, such as chatbots and virtual assistants, to answer learners' questions and provide guidance in a conversational manner. This application is commonly referred to as conversational AI. NLP is a field of AI that allows machines to read, comprehend, and derive meaning from human language in a thoughtful and useful way. NLP is currently being used in a wide range of applications. NLP interfaces will likely be deeply embedded into course products in the future, allowing learners to interact directly with and respond to learning activities. So, rather than being passive experiences with static learning content, activities will be structured around conversational exchanges with a host application that guides the learner through the learning experience and provides suggestions for further information. These ancillary activities could possibly reside outside the defined boundary of the course experience.

AI-Generated NLP Example

Consider a health care course that uses AI-generated NLP. The learner first speaks into an instructional voice-recognition system to record patient information. The system uses NLP to transcribe the spoken words into written text and processes the text to identify and extract important information. For example, if the learner says, "The patient reports severe headaches and dizziness over the past week," the system recognizes "severe headaches" and "dizziness" as symptoms and "over the past week" as the time frame. NLP algorithms understand the context of the language used. If a learner says, "The patient denies any chest pain or shortness of breath," the system interprets that to mean that the patient does not have these symptoms. The extracted information is then encoded into standardized medical terminology and structured so it is easy to search and analyze. For instance, "severe headaches" might be coded to a specific medical term such as "migraine." The structured data can then be integrated into the patient's electronic health record, making it easier for health care students to review and reference in future consultations. The insights generated can help learners make more informed care decisions.

5. Gamification

AI-Based Gamification Example

Consider a language learning course that uses AI-generated gamification. The AI system develops a personalized learning pathway based on an initial language proficiency assessment. The system might start with basic vocabulary and grammar if the learner is a beginner. If the student is more advanced, the AI might start with more complex sentences and conversation practice. The AI tracks performance as the learner progresses through the lessons. If the learner is doing well, the AI can increase the difficulty of the exercises, keeping the student challenged. If the learner is struggling, the AI can decrease the difficulty or provide additional practice in the areas that are challenging. The system might ask the learner to complete puzzles or play word-matching or conversational role-playing games to increase the element of fun. The AI could even create these games dynamically based on the student's current learning objectives. The AI provides instant feedback on the learner's performance. The student might earn points, badges, or progress on a leaderboard for answering questions correctly.

6. Predictive Analytics

AI algorithms can analyze data from learners' interactions with the course material to identify areas where they are struggling so instructors can provide targeted support. AI performance prediction models are often used to identify learners who are at risk of abandoning the course, create adaptive learning pathways, and optimize course design. In a recent study involving online undergraduate engineering students, an AI performance prediction model with embedded learning analytics was used to examine the differences in students' collaborative learning with and without the support of AI-based technology:

Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen[ed] student satisfaction about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.

AI-Generated Predictive Analytics Example

Consider a computer science course that uses AI-generated predictive analytics. Data about each student's background (prior knowledge of computer science, grades in prerequisite courses, and performance on an initial diagnostic test, for example) is collected at the course onset. As the course progresses, more data on the learner's performance-including assignment scores, test grades, and class discussion or collaborative project participation-is collected. Data about the student's learning behaviors (how much time the student spends on different course materials, which resources the student accesses, and when the student is most active) can also be collected. The AI system can analyze this data using machine learning algorithms to identify patterns and build predictive models. For example, the system might find that learners who spend more time on certain types of practice problems or who participate more actively in discussions tend to perform better on exams. The predictive models can be used to identify learners who are at risk of falling behind in or failing the course. If a student's performance and behaviors align with past learners who struggled, the system can flag the learner as at risk. The course instructor or the AI system can implement timely interventions-including personalized feedback, additional resources, tutoring, or peer study group recommendations-based on the predictive analytics.

7. Content Creation

AI-powered tools can help instructors automatically generate high-quality course content, such as quizzes, assessments, and simulations. So, for example, a course designer could develop highly engaging course content simply by providing a generative AI engine with a few text prompts that are aligned with student learning outcomes. The engine not only provides suggestions for content, but in some cases, it also creates the experience and pathway. Coursera recently introduced an early prototype of this technology at its 2023 annual conference. Other examples of this technology are surfacing in the corporate eLearning space:

Personalized quizzes, flashcards, and summaries that are automatically generated

eLearning content that is adapted based on learner data and interests

Automated text summaries to make content easier to digest

Chatbots and virtual assistants that provide personalized learning materials based on learner data

AI-Based Content Creation Example

Consider an online statistics course that is created using generative AI. If a diagnostic assessment indicates that a learner already has a good understanding of descriptive statistics, the AI could skip or shorten this module and focus more on inferential statistics. As the learner progresses through the course, the AI will continuously adapt the content. For example, if the student is struggling with the concept of statistical significance, the AI might slow the pace and provide additional learning resources, such as generative tutorials, infographics, or interactive activities. The course could incorporate AI-powered interactive elements to enhance the learning experience-perhaps a chatbot that answers learners' questions or an ITS that provides guided problem-solving exercises. The AI can also use the analytics to improve the course design for future learners. If the data shows that many students are struggling with a certain concept or that a particular learning resource is rarely accessed, these course elements could be revised.

8. Assessment and Feedback

AI-Generated Assessment and Feedback Example

Consider an English composition course that utilizes AI-generated feedback and assessment. The AI assesses a pre-course essay to determine a learner's writing abilities, considering factors such as grammar, punctuation, spelling, sentence structure, coherence, and vocabulary. Based on this initial assessment, the AI customizes the course pathway. If a student is struggling with punctuation and sentence structure, for example, the AI might start with modules focusing on these areas. As the learner submits assignments throughout the course, the AI assesses the work, using NLP to evaluate not just the grammar and spelling but also more complex aspects of writing, such as coherence, argument structure, and evidence use. The AI provides immediate, detailed feedback on each assignment. If a learner tends to write long, complex sentences that lack clarity, the AI might suggest that the student try using shorter, simpler sentences. The feedback is not just corrective but also formative, guiding the learner on how to improve. The AI tracks the learner's progress and performance, adjusting the course pathway accordingly. If the student shows improvement in weak areas, the AI might introduce more advanced modules. If the student continues to struggle, the AI might provide additional practice assignments. At the end of the course, the learner is provided with a comprehensive assessment. This evaluation details how much the student's writing abilities have improved and the areas the student should continue working on.

9. Learning Analytics

AI-Powered Learning Analytics Example

Consider a data science course that utilizes AI-generated learning analytics. The AI monitors the student's performance on assignments, projects, and quizzes as the learner progresses through the course. It records the learner's scores on each activity, the time taken to complete, the number of attempts made, and other relevant metrics. The AI also tracks the learner's engagement with the course, including how often and when the student logs in, how much time the student spends on each module, which resources the student accesses, and whether the student participates in discussions or uses other interactive elements. The AI system uses these data points to generate learning analytics, providing insights into the learner's performance, progress, and learning behaviors. For example, the system could identify that a learner tends to score lower on assignments related to machine learning or that the learner is most active in the evenings. The AI system can also use the analytics to predict future performance or potential challenges and suggest extra practice or tutoring.

10. Resource Allocation

AI-Supported Resource Allocation Example

Consider an English literature course that uses AI to improve resource allocation. As discussed above, AI can use NLP and machine learning to assess various aspects of a student's writing, including the student's use of grammar, punctuation, spelling, and sentence structure, as well as some of the more complex aspects of writing. For example, the AI might highlight a run-on sentence and suggest how to break it up or point out a weak argument and suggest how to strengthen it. Not only does this immediate feedback contribute to the student's learning, it also allows the instructor to reallocate some of the time that would have been spent manually grading and providing detailed feedback on essays and other writing assignments to instruction. AI can also automate and personalize lesson planning. For instance, if the AI determines that a student is struggling with analyzing symbolism in literature, it could automatically generate a lesson focused on this skill, including relevant readings, lecture materials, and practice exercises. The AI could then generate quizzes to assess the learner's understanding of each lesson. The quizzes could be automatically graded, and scores and feedback would be provided instantly.

Conclusion

Notes

1. A. J. O'Connell, "The Blurry Definitions of Adaptive vs. Personalized Learning," Campus Technology, December 20, 2016; Patsy Moskal, Don Carter, and Dale Johnson, "7 Things You Should Know About Adaptive Learning," EDUCAUSE, January 4, 2017.

2. David Karandish, "7 Benefits of AI in Education," THE Journal, June 23, 2021; Richard Adams, "Ofsted Under Fire in Its Own Survey of Teachers' Wellbeing," The Guardian, July 21, 2019.

Robert Gibson is Dean, ITAS, and Director of Instructional Design at WSU Tech.

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10 Ways Artificial Intelligence I﻿s Transforming Instructional Design

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Artificial intelligence (AI) is providing instructors and course designers with an incredible array of new tools and techniques to improve the course

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ChatGPT Assignments to Use in Your Classroom Today

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ChatGPT Assignments to Use in Your Classroom Today

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ChatGPT Assignments to Use in Your Classroom Today

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Indecision About AI in Classes Is So Last Week

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Indecision About AI in Classes Is So Last Week

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Inside Higher Ed | Higher Education News, Events and Jobs

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Many faculty members across North America have sat on the sidelines in 2023, hoping for someone or something to keep generative artificial intelligence out of their institutions. The primary lesson we all learned, however, was that no one can save us from AI but ourselves.

Many of our colleges' students are already using generative AI-without guidance-and many recent graduates say they feel unprepared for jobs because of AI. Businesses of all types have embraced generative AI, and significantly more say they will in the coming years. The trend seems likely to accelerate because early research suggests that AI can save workers time and improve the quality of their work.

So, as we look forward to starting a new year, the academy can no longer live in denial. We must help our students learn to use generative AI in effective, ethical ways in their learning and, eventually, in their careers and lives as citizens. In other words, the academy must step off the sidelines and into the scrum, especially as AI continues to change rapidly.

What Instructors Can Do Now

We've all received abundant advice about how to prepare students and ourselves for using generative AI in class, and many instructors have used the past year to learn new skills. But if you are still feeling uncertain-or overwhelmed-with the pace and scale of change that generative AI is bringing, that's OK. You can still take some easy, concrete steps to begin moving your classes and students into the age of generative AI.

In the examples that follow, we offer both some basic approaches ("good"), as well as more nuanced ways ("even better") to integrate generative AI into teaching and learning. The key is to start making changes now.

Good: Create syllabus language about AI use. Yes, this is basic, but including guidelines and expectations for how students may and may not use generative AI in your classes is crucial. With clarity and transparency about what to expect, students will be better able to make good decisions. Examples of syllabus language abound. The University of Toronto and the University of South Florida offer examples you could adapt for your own class.

Even better: Draft a plan with students. Add a statement to your syllabus that the class will collectively develop a plan for appropriate class use of generative AI. Empowering students in the process will improve their motivation to follow the policy.

Good: Plan conversations about AI. Help students understand how generative AI tools were created, how they work and why they must be used with caution. Include topics such as accuracy of information, stereotyping and biases in generative AI; appropriate use of generative AI; data privacy; and equitable access to generative AI. Kathryn Conrad's Blueprint for an AI Bill of Rights for Education and the AI Pedagogy Project are good starting points. The Center for Teaching Excellence at the University of Kansas also offers materials on helping students understand biases in AI and the ethical use of AI in writing assignments. The emerging field of explainable AI can also provide areas for discussion.

Even better: Embed exploration of ethical practice into assessments. Create an assignment that asks students to explore the ethics of generative AI use in your discipline.

Good: Create an exploratory assignment. Identify an assignment in which students must use AI tools. This exercise will help students-and you-learn about the workings of AI. It will help you better guide students in its use and generate ideas about how to adapt future assignments. The Center for Teaching Excellence at Kansas has curated a list of tools you might consider, or you can find your own at such sites as Futurepedia and There's an AI for That.

Even better: Take this approach with all your assignments. The more opportunities students have to develop facility with generative AI during structured activities, the better they will understand their role and responsibility in using the technology.

Deepening Your Practice

Once you have the basics down, consider deepening your use of generative AI as a teaching and learning tool. Here are five approaches that we and colleagues have found to be helpful.

Evaluate assignments through the lens of generative AI. Consider which activities are most vulnerable to misunderstanding, misuse or bias if students use generative AI. Evaluate how you might better integrate AI into those assignments, focusing students' attention on ensuring responsible practice. You might have a chat bot create questions for class discussion or ask students to use different chat bots for the same assignment and report back to the class about their experiences.

The Center for Teaching Excellence at Kansas provides an example of how Bing Chat can tutor students in a research and writing assignment. Ethan Mollick, an associate professor at the University of Pennsylvania who studies and teaches innovation and entrepreneurship, also offers examples of using AI in assignments. Anna Mills, an English instructor at the College of Marin, has curated an extensive list of AI-related teaching materials. Sites like AutomatED, the Sentient Syllabus Project and the online community AI in Education provide additional approaches. If you are feeling ambitious, you can even use ChatGPT to create a class.

Add a methods or reflection component to assignments. Academic research usually contains a methods section in which authors explain how they gathered and analyzed data, as well as how they arrived at their conclusions. Faculty members can treat generative AI as another method for completing tasks or assignments and arriving at conclusions. Students could describe their use of generative AI just as they would any other method. APA Style, the Chicago Manual of Style and the MLA Style Center have all published guidelines on citing and acknowledging use of generative AI.

Alternatively, instructors could have students briefly reflect on the approach they used for their work. How did they use generative AI? How did they adapt the output so that the work became their own? These practices encourage transparency and also help students improve their metacognition, the understanding of their own learning and thought processes.

1. Allow students to work on assignments and activities in class using AI. Work performed in class will allow you to observe and guide students to use AI tools in valuable and appropriate ways. Assignments or quizzes must be short so students can complete them during limited class time. They should also be low stakes, as some students don't work well under time pressure. If you need to free up class time for that type of work, consider a flipped class model, which offers flexibility for interacting and observing students.

2. Do oral checks of students' understanding. If you are concerned that students aren't doing assignments on their own, set up short individual meetings. Ask students to lead you through the process of their work and to explain key concepts, ideas or methods. As they do, it is usually easy to tell who has done the work and who hasn't. These meetings don't have to be long-five minutes, perhaps-and they can often be done in class. If you suspect problems, set up a longer meeting with the student to discuss academic integrity.

3. Adopt authentic assessments. Authentic assessment lets students apply their developing knowledge to real-world situations, and that contextualization can improve relevance and motivation. Such assessments can take many forms, depending on the discipline. Generally, authentic assignments apply content in ways that students are likely to encounter in their careers, that allow students to share their learning outside the class, or that encourage students to connect disciplinary thinking to other fields, their own lives, or a general audience. For example:

Students in a chemistry class create posters about how chemical interactions affect everyday life (hand washing, water purification).

Students in a psychology class write an op-ed article applying the principles of psychology to inform an event in the news.

Students in a journalism class work with a nonprofit agency to create messaging about mental health resources for high school students.

Students in a physics class create a poster describing what might have destroyed a deep-water submersible vessel.

Students in a biology class hold an end-of-semester poster session for which they create displays and activities to help attendees learn about threatened species.

Students in a film and media studies class create video and social media messages about the impact of digital literacy and fluency on teen deaths by suicide.

Generative AI offers powerful new opportunities to expand authentic assignments and infuse them with technological skills that students will need in careers and their daily lives. In each of the examples above, generative AI could initiate ideas, provide examples, create images and illustrations, design posters and brochures, and draft materials. You can also use it to create discipline-specific case studies or interactive scenarios in which students grapple with real-world problems. Or you could challenge students to envision using generative AI in various professions, both to learn more about potential careers and to consider how the technology might change a profession. The teaching center at South Florida has created a document on how to approach these areas.

Just Another Tool

Increased familiarity and facility with generative AI will help you and your students see it as just another technological tool, not a fatal foe. AI hasn't broken education. Neither did the internet, smartphones, Wikipedia, digital search, Wi-Fi, ebooks or other technologies that have emerged over recent decades. Yes, teaching practices must change, and that will require time and resources. But colleges and universities, and we as academic professionals, should consider this work to be an investment in the future that will help shape the standards of a new AI-infused world.
Doug Ward, Alison Gibbs, professor teaching and director of the Centre for Teaching and Support Information, the University of Toronto Tim Henkel, assistant vice provost for teaching and learning, the University of South Florida Heidi G. Loshbaugh, senior research associate, the University of Colorado at Boulder Greg Siering, director of the Center for Innovative Teaching and Learning, Indiana University Jim Williamson, director of educational technology systems and administration, the University of California, Los Angeles Mark Kayser, instructional designer, the University of California, Los Angeles associate director, Center for Teaching Excellence, and associate professor of journalism and mass communications, the University of Kansas

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Professors and Administrators From Five Major Public Universities

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Advice about AI in the classroom for the coming new year (opinion)

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Professors and administrators from five major public universities provide advice on how to get moving ahead with AI in the classroom right now. Many faculty members across North America have sat on the sidelines in 2023, hoping for someone or something to keep generative artificial intelligence out of their institutions.

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Designer Joe Alterio writes about the changing nature of creativity in this article, reflecting on how AI may change design. This may prove useful as you consider the design of your college classes.

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Thriving in an era of scarcity

One of a series of posters I did back in 2004, if you can believe it.

Robots, for all of their futuristic aura, are actually totems of the past. Their vision is always backwards facing, and this is especially true for Gen AI. In a dadaist, Max Ernst collage sense, all AI can do is generate the new by combining the old. Recently, Annie Leibovitz caused a bit of an uproar when she said she wasn't concerned about AI. But putting aside the bad taste of some rich person telling The Poors not to be concerned, she kind of has a point. Technological leaps will always happen and creatives always adapt. However, if you are a creative who is mostly just regurgitating the things that have come before, then you have a large reason to be concerned about the current state of the creative industry. I believe what undergirds much of the fear of this disruption is that fear - that deep down we have not really been that creative for a long time.

Zooming in, we can now understand that we are living during a brief period of time where those in a creative space were forced to adapt to a technical space for a short time. Into this space flowed "UX", but far from being a universally understood and permanent concept, it turns out that this was just a transitionary period.* Like many transitionary periods there are things assumed to be stable that are not, and things assumed to be transitionary that end up being more permanent. For a small time, software needed an interpolation layer to translate human intent to data. The time of needing that layer is ending.

Human beings are in general terrible with change. Our short time on Earth, combined with our narrow focus and influence of those closest to us, make us remarkably ill-suited for long-term planning and vision. We make up for this by being wildly good at adapting. If I am optimistic in general, it's because our history can be seen as one long pivot, from problem to solution, over and over again. We don't always get it right every time, but we get it right enough to survive, and on occasion to thrive.

As the photographic industry was the refuge of every would-be painter, every painter too ill-endowed or too lazy to complete his studies, this universal infatuation bore not only the mark of a blindness, an imbecility, but had also the air of a vengeance. I do not believe, or at least I do not wish to believe, in the absolute success of such a brutish conspiracy, in which, as in all others, one finds both fools and knaves; but I am convinced that the ill-applied developments of photography, like all other purely material developments of progress, have contributed much to the impoverishment of the French artistic genius, which is already so scarce.

- Charles Baudelaire, On Photography, 1859

What does it mean when a tool becomes irrelevant? For one, it means that those that are only good at that tooling are in danger. I have tried to warn any designers I have been lucky enough to mentor in my life that getting good at a tool is limiting your value only to the lifetime of that tool. If you are young and impressionable, it's easy to assume that the current hammer-and-drill set will be with you your whole life, and it's an excellent idea to become a master at the tool, to attend splashy conferences about that tool, and advertise your sharp skillset with that tool as evidence you are valuable.

History teaches us otherwise. Ask ActionScript experts how they feel about being ActionScript experts right now.

The reality is that the functional low-hanging fruit of digital creativity has been completely and total mono-cultured to the point of near obsolescence, something I detailed here in a previous piece. The roads have been laid, the patterns have been established. Additionally, unless the tool is open source, your focus of tooling makes you nothing more than a thane to someone else's bottom line. You are using a product that makes money for someone else, much like posting creative work for free on a social media site that rakes money from your content is a fundamentally exploitative enterprise ( NB: I am not immune, and am not missing the irony).

But making Zuck a few fractions of fractions of a penny for posting a photo of your cat is, I would argue, a less fraught concept. Becoming an expert in someone's playground is at core - and I use this term judiciously - a form of digital feudalism. And like any large commodified solution, those spaces have been dominated by a few large and appalling players who don't care about art, creativity, the user, or really anything but making those fractions of pennies. The lords have made the field, and instructed us to plow. So we plow.

However, seasons change: summers become autumns, and autumns become winters. And with each new season, new dangers and opportunities arise.

Some people think this is evidence that Adobe ceded victory to Figma. Don't forget: Adobe is the one who backed out of the deal.

Honestly, I'm not the best designer in world. I'm a solid B+ on most days. I get the job done, but I don't think I'm a designer to remember. I don't really mind that, because I personally suspect most of history rewards my phenotype - The Survivor. Many may inspire the masses, some make a ton of money, and a few invent new techniques, but no other but The Survivor have the ability of meeting the moment. I basically shift my shape to fit the current creative need. I can do a little bit of a lot of things. It's certainly not glamorous, but it's a very useful skillset that I think we are unfortunately moving into, creatively: the Era of Scarcity.

First of all, let's understand that, for the past ten years, the world of creativity in the digital space (I often use the shorthand "web" as that is my historical experience, but for the purposes of this argument, we can read 'digital' here as web, mobile, gaming, streaming, etc) has evolved to be something a little like this:

Here, we can see the divisions of digital labor is a grouping of specialized sectors, all with their own lanes. This is quite different from how work in the digital space used to be, before around 2006. Back then, many hats were worn by a creative, mostly because:

1. There were no best practices

2. There wasn't enough of us

3. We were all just kind of making it up

Those striations of labor you see above were created because as more money and more players ran into the industry, projects got more complex, more money was a stake, codebases became far more complicated, and mobile and search became things. Suddenly, you couldn't do the architecture on a project as well as handle the visual design: you had too much to do! And a creative person went from being just the sole source of creativity to the archetype of The Designer.

In order to make room for all these new people and new processes, creativity had to be winnowed and narrowed, codified and put into tickets, to the point of total abstraction. And once you codify something, you can commoditize and monetize it. Hence, the rise of the big design tooling platforms, built to serve The Designer. The same thing happened with film, animation, photography, and illustration.

There's another factor at play, too: in a world of chaos, order is a salve. We became convinced that Big Data could provide all the answers. In our own small way, we in the UX community fell into this, too, so dazzled by the idea of perfect data-driven tokens and perfectly-snapping responsive grids.** In this space rose Figma and it's less-successful ilk, here to monetize a trend of standardization across all channels. The systemization of everything smoothed the rough unsure edges. In an unsure era, what could be comforting but the cool standardized variants of a design system?

A tool that helps you perform your job at a higher velocity is nothing to sniff at. But here is what is not advertised: standardized tooling, unlike real creativity, can be done by Robots.

I am, in general, not an early adopter.

I resist upgrading my OS, I don't run out and buy the newest gear, and I take a certain schadenfreude when I get to watch something new and so obviously stupid go up in flames.

When the pointy heads started talking about how Gen AI was different, I was mostly unimpressed. I thought that LLMs were mostly just predictive text, based on a dataset of previous use. Cool, but not anything to lose your mind over. But what AI has done for me in the last 6 months has both chilled me to the bone and made me wildly optimistic about long term trends.

While we can deeply mourn the loss of jobs and the disruption of the lives of millions, we need to admit that the advent of generative AI completely destroys the middleware commodification layer of UX. Every design system built in Figma, every standard pattern in a prototype, every Github ticket for a recommendation of hex color replacement can be done by a bot. And what's more, we don't need to wait for a superior Gen AI to create this volcanic sea change. It's here, now. There is a reason why LinkedIn is the most depressing website in the world right now.

The insertion of AI into a common design space does some weird shit. Suddenly, much of the "break" part of our jobs has been commodified. All that time doing dumb searches and poking around for stuff? That's kind of... gone, now. The AI gotchu. The Bot is your constantly-awake, extremely eager, probably-spinning-on-dexadrine intern. The Bot does not tire. The Bot is not bothered by your stupid syntax. The Bot doesn't have opinions on what you order for dinner. The Bot is here, and ready to help. Forever.

In some ways, this is very nice. If you have an idea and you want some easy lifts to get you to increase velocity ("Give me the 10 most common patterns for Payment navigation"), it's there for you. However, we must face that it will lay waste to the hundreds of thousands of very well-meaning archetypical Designers, who were taught that all they had to do was learn the 250 desk patterns, and 150 mobile patterns, and they had a ticket to the middle class. That archetype is suddenly kind of useless. This is already happening, and there is no turning back. I grieve it from a human perspective, but I am not surprised from a market perspective.

Max Ernst's Une Semaine de Bonté (orig. 1934)

But before you tear your hair out, let's be clear. This is not the end of creativity. This is the end of The Designer. This is the end of a certain type of digital craft. And they are two very different things.

We are coming off of 40 years of regurgitation being the dominant culture output. Remixes, reboots, nostalgia-infused look-backs, and the outright theft of the past - Kurt Anderson's Evil Geniuses (so recommended, go get it right now) lays out our current predicament:

In those years, ... America swerved away from the new on two distinct but intersecting levels. In culture, it fell into a mass nostalgia that became a cultural listlessness, a slowing of the rate at which life looked, felt and sounded new - Americans from the 1950s and 1970s appearing as if from different planets, but Americans from the 2000s and today looking not all that dissimilar.

My gut feeling is that we are tired, so tired, of this as a core form of creative regurgitation. How many Ghostbusters reboots are needed? How many rereleased albums are necessary? How many times can the AI puke together a design that looks vaguely like something you've seen previously, before you start yearning for something else? We're past the first blush, and well into the Early Adopters phase of AI. And already, we're starting to see fatigue. Deepfake porn, cheery, meaningless illustrations, soulless yet artful photos: these are as easy as a prompt, and like any market logic, the values of these items has raced to near zero. And whenever a market nears zero valuation, there is an opportunity in the zag when everyone else is zigging.

These two forces, the wholesale destruction of creative craft wrought by standardized tooling, and then AI bulldozing that dataset, have made a massive space for real humans again. If you were a writer of utopian fiction, you could not write a better opening. I'm starting to believe that we are at the precipice of a new renaissance of design, art, music, and idealism. And it's all because of these stupid enterprise tools. These bots will completely commoditize the tooling masters that we have been in thrall of for so long. Like Gandalf coming at the Dawn of the Third Day, the AI revolution will sweep away all the big nasty tooling monsters in one brutish swoop. It's going to be awesome. And scary.

A large and looming conversation we will be facing soon is whether actual open, human-generated, creative dialog that is fraught with deeply offensive and problematic content will be preferable over "family-friendly" AI channels. I don't know how it will break. However, like any Economics 101 class teaches us, the party with the endless and cheaply made supply will dominate-but stay in-the low end of the market. My belief is that Humans will occupy the upper market, but will have to be fundamentally unique enough to generate upper value.

In my old job, I managed people that managed design systems. It was fun (the people made it fun!) but it wasn't creativity, per se. In my new job, one day I do UX, one day I do illustration, one day I do sound design, and one day I give advice to our user researcher. It's everything, all jumbled up at once. Figma can't do this, and neither can ChatGPT. This is where my value now is, and I kind of love it. But it's harder.

Here's what I think the new modality of digital creative is.

What's more, bringing people up will be more like creative collaboration than top down management, as you bring in people that can fill in the creative gaps that you desire and like, like a true creative collective. But again, the value will be an authentic creative vision, which is actually pretty hard to do.

I think the debate about AI Doomerism is a little silly. When people fret about the robots taking over, what they are actually fretting about is whether the humans will be dumb or evil enough to let the robots take over. That's not to negate the issue (my own p(doom) is a solid 11%), but in this debate, I firmly believe we'll be okay because I am a big booster for humans, when all the chips are down. The bots will prove themselves to be just good enough to be ignored. It will be a very hard transition, in which new skills will need to be learned (or maybe relearned), old habits will have to die, and a new paradigm will have to be be built.

But I think we got this.

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*We are still early in the process, and I reserve the right to proffer a mea culpa if everything turns out differently. **Part of it, I suspect, is that the nascent and forever-present idea inside of all creatives, that maybe we're somehow admired and needed by our colleagues in the hard sciences: the scientists, the engineers. Maybe, if we made our craft like them, we could finally be accepted. It didn't work. I suggest we give up and lean into being the weirdoes we are already.

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Robots, for all of their futuristic aura, are actually totems of the past. Their vision is always backwards facing, and this is especially true for Gen AI. In a dadaist, Max Ernst collage sense, all...

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Check out this clear and concise guide to prompting for educators from Harvard University's IT Department.

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Generative AI

It's very important to keep in mind that AI-generated content can be inaccurate, misleading, entirely fabricated, or offensive, so be sure to carefully review any work containing AI content before you use or publish it.

You don't have to get everything into your first prompt. Try starting with a basic question and adding to it over time. Change the wording or tone or add more context and specificity to guide the AI toward the output you're looking for.

Chat with the AI as if it's a colleague or teammate and you're working on a project together. Give feedback - tell it which parts of the output were useful and which parts could be improved. If you notice it got something wrong, tell it so it can correct its mistake.

Please note HUIT is not responsible for content on third-party websites:

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Getting started with prompts for text-based Generative AI tools

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The information, sentences, or questions that you enter into a Generative AI tool ("prompts") are a big influence on the quality of outputs you receive. After you enter a prompt, the AI model analyzes your input and generates a response based on the patterns it has learned through its training.

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A free, self-directed course on AI from Microsoft!

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Ethical Implications of ChatGPT in the Educational Setting

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Ethical Implications of ChatGPT in the Educational Setting

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Understanding the current state of technology requires understanding its origins. This reading list provides sources relevant to the form of generative AI that led to natural language processing (NLP) models such as ChatGPT.

Credit: Qpt / Shutterstock.com © 2023

Generative artificial intelligence (AI) is in a renaissance amid a profusion of new discoveries and a breathless frenzy to keep up with emergent developments. Yet understanding the current state of technology requires understanding its origins. With the state of AI science changing quickly, we should first take a breath and establish proper footings. To help, this article provides a reading list relevant to the form of generative AI that led to natural language processing (NLP) models such as ChatGPT.

Artificial neural networks have been around for more than sixty years. We can start in 1958, when the perceptron was initially implemented on a computer. For the first time, an algorithm used computing power to ingest labeled sets of data and classify them into categories. Nearly thirty years later, in 1986, backpropagation was introduced as an algorithm for training a neural network to learn from its mistakes. This advancement enabled multilayer perceptron networks to learn in nonlinear ways. In 2017, the Transformer was created as a neural architecture that improves on the memory limitations of past recurrent models. This efficiency gain enables a network to better handle the context and relevance of the information it has been provided. All three of these technologies remain relevant and used today.

There is a deep historical record about practical developments in neural networks, and the theoretical work supporting them includes centuries of rigorous discoveries in math and biology. Interestingly, despite this rich context of empirical and conceptual work, neural networks have not received much popular attention. AI developments have broken into the collective consciousness only occasionally-for instance, when the IBM supercomputer Deep Blue beat world champion Garry Kasparov in a chess match in 1997 or, more recently, when Google DeepMind's neural network AlphaGo defeated Go grandmaster Lee Sedol in 2016. Instead of AI developments, computer and data networks have commanded most of the attention of technologists in the past few decades. While AI has been relevant and useful in our lives for at least the past ten years, the barrier to entry has been high, and its applicability to higher education has not been obvious. The time has come, however, for IT professionals to focus on AI in its various forms, not because it will solve all problems but because it will transform how organizations operate.

Neural Network Basics

Since this is a reading list, we'll start off with a video! How could we do such a thing? Because at this stage, only a human author can make such a contradictory statement. If we must condense decades of knowledge about neural networks, then it is necessary to focus on the fundamentals. Video is a helpful tool at the outset because neural networks are fundamentally visual. This is partly because neural networks are based on visual math (e.g., calculus, statistics, and linear algebra) and also because the multiple layers of networks are inherently multidimensional concepts.

Produced in 2018 by 3Blue1Brown, the video "But What Is a Neural Network? | Chapter 1, Deep Learning" occupies an important moment in time: before Transformers began to accelerate NLP models. It presents a classic challenge in AI development: how to handle recognition of handwritten numbers. This video articulates the fundamental components of modern neural networks including the concept of supervised learning. It also provides an example of the conceptual problem presented by hidden layers.

Now that we've seen a general representation of how a neural network functions, it is time to expand our horizon: there are many types of neural networks, each with particular strengths and weaknesses. Fjodor van Veen's list, "The Neural Network Zoo" (The Asimov Institute, September 14, 2016), will at first seem overwhelming, but the goal in reading about these is not to commit this information to memory. Instead, consider each network permutation as being illustrative of the many possible tools that can be used to solve different problems.

The first category of networks that we need to understand in greater detail consists of recurrent neural networks (RNNs), developed in the 1980s. RNNs allow for nodes to interact with each other, enabling the network to handle sequential data (e.g., words in a sentence must be in sequence to be understood). The interaction of nodes within the hidden layers enables the network, with sufficient training and fine tuning, to predict future steps in a data sequence. Eniola Alese's article "Understanding RNNs Using the Game of Chinese Whispers" ( Medium, April 13, 2018) is a helpful introduction to the subject.

In "And of course, LSTM - Part I: The ABC's of the LSTM" ( Medium, June 8, 2018), Alese introduces some of the challenges with RNNs-namely that they have memory problems. In 1997, the long short-term memory (LSTM) neural network was developed, solving this problem. LSTM introduced a memory state to enable the network to retain relevant information in order to understand context, while discarding information that is no longer needed.

While LSTM networks can handle much larger inputs, they are slow to train, and they are less efficient running on modern GPUs that are designed for parallel processing. Despite these limitations, LSTMs continue to be relevant and useful for some tasks (as are RNNs). Two decades later, Transformers were created to address some of these limitations. This technology has largely driven the current revolution in NLP. Transformers have some powerful advantages, including the ability to better understand the context of input data due to a self-attention mechanism that is more precise about word dependency and relevance. Jay Alammar provides a high-level conceptual overview in "The Illustrated Transformer" (June 27, 2018), whereas Peter Bloem gives a nice introduction from a programmer's perspective in "Transformers from Scratch" (August 18, 2019).

Definitions

Note: The definitions listed here are for concepts mentioned in the article. They do not cover all aspects of neural networks.

Fundamental Components

Hidden Layer: A generic term for all layers between the input and output layer. Different configurations of layers and neurons result in different types of neural networks.

Neuron: A multi-layer neural network node that receives multiple inputs with distinct parameters, which are processed through an activation function to produce various types of output.

Parameters: The weights and biases associated with each neuron in a network. These constitute the primary means to tune network functionality and behavior through training.

Perceptron: A single-layer neural network node that receives information (input) and, with the help of an activation function and parameters, produces a binary output.

Neural Network Types

Artificial neural network: A type of software that can independently learn at the expense of reduced transparency. In traditional software, a programmer defines fixed parameters and designs a structure based on logic. Neural networks, like other machine learning algorithms, are not deterministic in this way. Instead, they are probabilistic: they can handle novel tasks without explicitly being designed to do so. Training determines how the software operates, which means that a given neural network can behave very differently based on how it is trained.

Long Short-Term Memory (LSTM): A general improvement to the RNN design. Nodes can retain relevant, specific information from previous nodes' outputs. This is made possible with a dedicated memory mechanism that is discerning (based on training) about what information should be retained, discarded, or output at a given point in time. These networks are able to handle complex sequential data (e.g., several paragraphs of text).

Recurrent Neural Network (RNN): A network design in which nodes within the hidden layer receive partial information about what was processed by previous nodes. This allows for a type of short-term memory wherein a given node may have condensed information from a previous node's outputs. When combined with new input, this enables the network to make predictions about sequential data.

Transformer: A neural network design that ingests all the input it receives simultaneously, analyzes that information to determine which parts are the most relevant (self-attention), and then uses a basic feed-forward network to determine output. The problems of limited memory caused by handling input in a rigid sequential order (e.g., LSTMs and RNNs) are solved by a holistic understanding that enables the transformer to handle highly complex data types.

Neural Network Training

Backpropagation: Developed for supervised learning, an algorithm that computes gradients (essentially efficiencies) in accord with a loss function and propagates these calculations back through all network layers, adjusting parameters in order to improve network accuracy.

Few-shot learning: A variation of supervised learning in which very little labeled data is provided to the neural network. A sufficiently sophisticated neural network can interpret a well-curated limited dataset and accurately extrapolate future matches for data and labels it has not seen in training.

Reinforcement Learning from Human Feedback (RLHF): A training technique that can be used independently or in combination with other training techniques. The user helps train the behavior of the neural network by rewarding some responses over others, thus influencing the way the network modifies its own weights and biases.

Supervised learning: An ideal training technique for neural networks that are designed for well-defined datasets. Engineers will curate a set of data with labels, which in effect is like providing the questions and answers for a test. When the neural network processes this data, it adjusts its parameters based on its own results. If it succeeds in correctly labeling the data itself, certain connections between nodes will strengthen; if it fails, those connections will weaken.

Unsupervised learning: A quick way to train a network on things like grammar and knowledge without explicit labeling. Engineers curate a set of data and ensure that it is relevant and clean, but no labels are provided. The neural network produces value in this scenario when it is able to successfully identify meaningful patterns in the data. A loss function is used but, in this case, for optimization of the network rather than accuracy per se.

The Building Blocks of ChatGPT

With some history established, we can now review important articles published in the past few years. To start, note that OpenAI (the creator of ChatGPT) was a nonprofit organization founded in 2015 by well-established industry leaders with the intent to freely collaborate on AI. In June 2018, OpenAI published "Improving Language Understanding with Unsupervised Learning," which makes the case for unsupervised learning using Transformers. Unsupervised learning is the process of providing data to a neural network without telling the network how to understand the data (e.g., giving the network images of animals without labeling them) and configuring the network so that it can figure out, on its own, what is needed. In the case of GPT-1, 7,000 books were fed into the model to train it to accurately predict future words.

Eight months later, OpenAI announced GPT-2. This version demonstrated capabilities that both exceeded expectations and demonstrated emergent capabilities: ways of operating that the developers had not anticipated. This led OpenAI to do something that didn't garner a lot of attention outside of AI researchers: it restricted the release of GPT-2 due to fears of malicious use (e.g., generating spam, producing fake product reviews). In May 2019, a limited version was released, followed in August by a model at something like half its capability (measured in parameters), and in November 2019 the full GPT-2 model was released. Once again using very accessible language, OpenAI describes GPT-2 in "Better Language Models and Their Implications," which includes a section on policy implications and thus begins to grapple with the societal impact of this technology.

Six months later, in June 2020, OpenAI released GPT-3, without any staged release or restrictions to capabilities. Instead, OpenAI decided to create a for-profit arm and refrained from releasing the source code to GPT-3. The key breakthrough in GPT-3 is that after increasing the size and configuration of the model, OpenAI discovered that the model was now capable of few-shot learning: performing supervised learning with very limited data, enabling rapid adaptation. Thus, while the previously discovered practice of unsupervised learning continued to be followed in GPT-3, the supervised learning portion of training was dramatically improved. Unfortunately, OpenAI did not provide the same high-level summary as it had for GPT-1 and GPT-2, but it did make up for this change of tack with a very thorough technical paper: Brown et al., "Language Models Are Few-Shot Learners" (July 22, 2020). While this paper requires some knowledge in the field, it explains the relevance of this technological advancement.

In March 2022, OpenAI released GPT-3.5, with GPT-4 following just a year later, in March 2023. The chatbot product known simply as ChatGPT-launched on November 30, 2022-for the first time provided an easy, web based interface to the GPT-3.5 model. Something that is notable with these releases (particularly starting with the November 2022 reconfiguration of GPT-3.5) is the dramatic increase in emergent capabilities. This time, OpenAI did not publish a high-level overview for ChatGPT. Instead, the web page "Introducing ChatGPT" simply describes what the developers saw as some neat but essentially "iterative" changes.

In February 2023, Stephen Wolfram, a pioneer in the development and application of computational thinking, wrote a very thorough blog post: "What Is ChatGPT Doing . . . and Why Does It Work?" Wolfram summarizes how ChatGPT works. It is a long read with some math, but it is an outstanding resource.

Meanwhile, AI researchers became increasingly excited and concerned about what GPT was capable of. They started to notice that entirely new capabilities were possible. Wei et al. described this phenomenon in "Emergent Abilities of Large Language Models" ( Transactions on Machine Learning Research, August 2022). The co-authors noted societal risks, including behavioral and sociological concerns.

NLP Model Ethics

NLP models like ChatGPT raise a variety of ethical questions. While OpenAI has operated with intentionality with respect to ethical concerns, monetary and corporate interests likely mean that the company's ethics are not guaranteed. Considering the exploding growth of alternative generative AI models, however, many of these products will face much less scrutiny and may have far more significant ethical lapses. But before we can discuss ethics particular to AI, we should consider an ethical framework. Hegel's theory of responsibility is a helpful mooring: social context is essential for interpreting ethics, and for any given action, it is also important to consider responsibility for the consequence of this action. Further, knowledge of consequences is imperative for interpreting responsibility.

Starting with the ethical question of bias, OpenAI has put in significant work to train the GPT model to be mindful of social context and to manage for bias. This is quite unlike Microsoft's infamous 2016 chatbot Tay, where the problem of AI bias took on an extreme form. The relatively positive results of GPT demonstrate the importance of supervised training. A pressing concern is that competing models have not been trained as intentionally; in addition, "desktop" generative AI models, which are accessible to anyone, could become unethical by design or accident. In "Language (Technology) Is Power: A Critical Survey of 'Bias' in NLP" ( ACL Anthology, July 2020), Blodgett et al. provide an excellent critique of bias research in NLP models and make a compelling case for why much more work is needed to improve bias recognition and training in NLP. In "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" ( FAccT '21 Proceedings, March 2021), Bender et al. address ethical concerns about increasing NLP models to sizes that may amplify bias and create adverse environmental impacts.

Misinformation is another significant concern. Generative AI models, including GPT, "hallucinate" or confabulate by making up information. This is particularly jarring because the confabulations can appear authoritative: made-up sources sometimes reference real authors but fake titles and even specific page numbers. Such faults can be addressed through supervised training: try asking an NLP model about the company that produced it, and the results will be very consistent. We have also seen GPT's dramatic reduction in confabulation errors as new releases come out, thanks in part to reinforcement learning from human feedback (RLHF), in which users rank responses in terms of accuracy. Meanwhile, a product like Bing Chat has attempted to address this issue with a "creativity" setting with the intended goal of confabulation. This is an ethical position that requires nuance, since it posits that when an NLP makes up information, it is being creative rather than misleading. In "Training Language Models to Follow Instructions with Human Feedback" (March 2022), Ouyang et al. make a compelling case for using RLHF in NLP.

A similar consideration is the impact that this technology has on society. We continue to struggle with the ethical impacts of social media, a technology that is also driven by AI. Since this different form of AI has been used to disseminate fake information, it is apparent that NLP models can be used in a similar manner. Social media AI is, ironically, designed around controlling our attention. One of the consequences of controlling attention is algorithms that promote extreme viewpoints to keep users engaged. This, in turn, leads some people to create socially siloed cultures. When NLP bots are pervasive and anonymized across platforms, how will that change how we interact with each other? What does it mean for our understanding of reality when so much of our society is digital and when the digital output of AI is indistinguishable from the digital output of a human? Will NLP models take a step further than social media and create a distinct AI culture? Hunt Allcott and Matthew Gentzkow provide a thorough account of the role of social media in influencing different kinds of social behaviors in their article "Social Media and Fake News in the 2016 Election" (Journal of Economic Perspectives, Spring 2017).

Privacy and security are complicated ethical concerns for NLP, since we understand very little about the possible consequences of emergent capabilities. Spam and phishing are obvious concerns: grammar mistakes will no longer be a telltale sign of fake solicitations. There are also immediate concerns about the data directly shared with the model (and breaches have already occurred). Yet the ethical questions run far deeper. To what degree can exposure to limited information about a person allow for excellent impersonation of that person, rendering so much of our security infrastructure invalid? To what extent can a model learn about an individual and accurately predict how that person thinks and acts in order to manipulate someone to take an action? How much information will be necessary to determine someone's relationships, habits, and preferences in ways that surveillance states could leverage? If the business model of social media AI is advertising (and thus, AI is influencing people to buy products), what business models will emerge for NLP? How will the mechanism of autonomy on the internet, sometimes used by humans in problematic ways, allow an effective mechanism of responsibility for AI? Indeed, autonomy and privacy are likely on the verge of reformation. Brundage et al., The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (February 2018), provides a comprehensive overview of various security problems posed by AI. While the report is long, it is essential reading and provides a framework that will hopefully be updated in the future.

The crux of AI ethics is accountability and transparency. A key component in neural network functionality is the hidden layers that lie beyond the direct control of programmers. When emergent behavior happens, the practical result is that a black box effect has been extended to the output layer: unanticipated behavior or capability has been realized. There is an important ethical distinction between not knowing how something is done and not knowing what the network is able to do. For every novel permutation of different outputs, we have no mechanism to evaluate the ethical implications of the consequences that may follow. Whether we follow Hegel or other ethical philosophers, understanding how these models produce specific results and understanding the full spectrum of what they are capable of producing is essential.

Technologists looking to use NLP models within their institutions need to establish an ethical framework that considers those concerns within their realm of agency and control. One step toward transparency is attribution: how and when do we attribute work to generative AI? Looking up a topic up on the internet, getting ideas, and writing about it is very different from copying sections from an article without attribution. Similarly, direct copying from content produced by generative AI tools requires disclosure, but using that content for ideation does not. Consider also the need for accountability. Blindly trusting in generative AI output is unwise and often unethical. AI output cannot be cited because it is not referenceable. Validating AI output by consulting reliable sources to arrive at a sound conclusion is surely reasonable. Ethical Guidelines on the Use of Artificial Intelligence (AI) and Data in Teaching and Learning for Educators (2022), published by the European Commission, is an excellent guide for the thoughtful use of AI in education.

Since I started this reading list with a video, how could I pass up the opportunity to end the same way? In the hour-long video "The A.I. Dilemma" (March 9, 2023), Tristan Harris and Aza Raskin, two cofounders of the Center for Humane Technology, excellently summarize the dangers currently posed by the AI field generally. They discuss what the future adoption of these tools may look like based on what we know now about the current capabilities of AI technology.

In Summary

The history of AI is entwined with the history of computing. While the importance of the internet is now widely accepted, the internet is a new technology by comparison with AI. The societal relevance of the internet languished for more than two decades after its creation, yet the incubation period for AI has been even longer.

AI is no longer a specialized or niche field of research with limited applicability. In the past decade, AI has been delivered, in one form or another, to consumers without the AI label (e.g., social media platforms, voice assistants, recommendation systems, photo editing). ChatGPT is an announcement to the world that the field of deep learning-that is, highly capable neural networks-is now immediately accessible to all. Perhaps more importantly, ChatGPT is a demonstration of the effectiveness of one type of AI tool that will encourage all of us to invest in and explore this expansive landscape of possibilities.

As many of us in IT organizations embrace AI tools in the coming years, we need to recognize the consequences of our actions. In time, AI will fundamentally challenge the way we understand reality. AI is a novel collection of technologies without historical parallel. Just as AI will not solve all problems, there is not one simple and ethical way to navigate AI. Instead, a rigorous analysis of ethics and metaphysics and a careful consideration of how IT organizations deploy AI will be required. IT organizations need to build on the ethical framework of equity and inclusion to ensure that this empowering technology experience of generative AI is transparent and accountable.

Notes

1. For the purposes of this article, neural networks throughout will refer to artificial neural networks.

2. The seminal 2017 paper "Attention Is All You Need" by Ashish Vaswani et al. gives an introduction to this subject. Despite the catchy title, this paper is quite technical and requires significant working knowledge of deep learning. Note that Google pioneered this Transformer technology and made it open source, along with other key AI technologies.

3. A. Ivakhnenko and V. G. Lapa's book Cybernetic Predicting Devices (Kiev, 1965) was developed in the Soviet Union and was the genesis of deep learning. For the first time, a mathematical foundation was established for how neural networks could successfully operate with multilayer perceptrons. While this is a technical work, in Chapter 4 the authors recognize the importance of Frank Rosenblatt's 1958 work with implementing perceptrons, and they provide a great contemporary understanding of the model. Following up on this work was Ivakhnenko's article "Heuristic Self-Organization in Problems of Engineering Cybernetics," Automatica 6 (1970), in which he introduced an algorithm to automate neural network construction and utilized the theoretical groundwork he had previously established with multilayer perceptrons.

4. A wonderful resource for more information on other types of networks is the blog series on deep learning written by Tim Dettmers, a staff member at NVIDIA, in 2015-2016. The first blog post focuses on visual AI (e.g., how convolutional artificial neural networks function). The second blog post provides a history of deep learning and discusses some of the math developments. The third blog post covers some of the fundamental building blocks of NLP models. The fourth blog post discusses different network training methods of the time.

5. This high-level summary of their work is quite accessible to nonexperts. For the technical version, see Radford et al., "Improving Language Understanding by Generative Pre-Training" [2018].

6. For the technical version, see Radford et al., "Language Models Are Unsupervised Multitask Learners" [2019]. An interesting way to look at this paper is to search for the term surprising, as noted by Benj Edwards in "Why ChatGPT and Bing Chat Are So Good at Making Things Up," Ars Technica, April 6, 2023.

7. The March 2022 initial release of 3.5 received similarly modest treatment by way of feature announcement.

8. Ethical philosophy is absent in many publications on AI ethics, and this often results in shallow ethical discussions. Furthermore, since 2021 several ethicists with robust philosophy backgrounds have been fired by both Google and Microsoft. See Daisuke Wakabayashi and Cade Metz, "Another Firing among Google's A.I. Brain Trust, and More Discord," New York Times, May 2, 2022; Zoe Schiffer and Casey Newton, "Microsoft Lays Off Team That Taught Employees How to Make AI Tools Responsibly," The Verge, March 13, 2023.

9. Georg Wilhelm Friedrich Hegel, Elements of the Philosophy of Right (1820). Hegel's thoughts on imputability are also relevant. Knowledge of what may occur as a result of one's actions is an important discourse when considering emergent behaviors in AI.

10. For a recent historical perspective, see Sofiya Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (2018), a groundbreaking book based on years of research about Google's search algorithms and the pervasive bias within them.

11. Oddly, for some famous confabulations that have occurred to date, companies disclaimed responsibility. This is a dubious ethical position because the only substantive function of NLPs is their output. This is like saying the company is responsible only when there is no problem! Since the summer of 2023, all major models now feature disclaimers about this issue.

12. Interestingly, ChatGPT works within an explicit knowledge cutoff date of September 2021. This is partly a practical matter, since pretraining has to begin and end at some point in time. It is also, however, intentional: OpenAI researchers understood that these models would soon be used to populate the internet with generated content and that training a neural network on content generated by other neural networks leads to model collapse. Similarly, if we end up "validating" the results of ChatGPT through web searches that are driven by other NLP models and lead to AI-generated content, does that really count as validation?

Brian Basgen is CIO at Emerson College.

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Understanding the current state of technology requires understanding its origins. This reading list provides sources relevant to the form of generativ

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7 Things You Should Know About Generative AI

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7 Things You Should Know About Generative AI

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The release of ChatGPT and similar AI tools that generate content including text, images, and audio has prompted both excitement and apprehension among leaders, faculty, students, and others in higher education.

Credit: TARASIRI / Shutterstock.com © 2023

Scenario

Over her many years teaching European history, Margaret Hilliard had been generally open to incorporating new approaches to teaching, certainly more open than those of her colleagues who bristled at any suggestion that their teaching methods could benefit from updates, including those rooted in technology. But when ChatGPT was released, Hilliard felt unprepared and deeply unnerved. Her courses had long featured essays as a central element, not only as a way for students to demonstrate their understanding of the course material but also as a regular activity to help students improve their writing. Hilliard stressed the importance of thinking broadly about historical events as well as the necessity of being able to clearly articulate those ideas. With ChatGPT, the dynamic in her courses changed, essentially overnight. Her students were suddenly producing longer essays that were better organized, grammatically impeccable, and always on time. At the same time, though, many of the essays were conceptually flimsy, lacking the kind of broad thinking she tried to draw out of her students, and the essays often included outlandish factual inaccuracies.

Hilliard spoke to her colleagues in the history department, as well as faculty who taught literature and sociology, and all were seeing the same changes in student assignments. A design instructor told her that he was wrestling with how to deal with student work that he believed was being generated by image-based AI. Meanwhile, campus leaders had begun issuing statements, policy updates, and other guidance about the use of generative AI tools for academic work, but those efforts seemed always to be a step behind the technology and how it was being used.

Talking directly and candidly with her undergraduate students-and conferring at length with her graduate students and TAs-Hilliard undertook to reorient her teaching to try to preserve the value of the essay without being naïve about the reality that generative AI tools were here to stay and would only get better over time. For some assignments, she required her students to use AI to create a first draft of a short essay and then critique that draft, looking for errors, omissions, and opportunities to improve the arguments. This enabled a different kind of learning for the students, including forcing students to carefully consider the prompts they feed into the AI tools, and it helped remove the stigma of AI in education. For other essays, she told her students to write their own drafts unaided and then use AI to generate an essay on the same topic. Students would then compare the two, seeking to understand how and why they overlapped and diverged; this process proved particularly beneficial for students whose skills with organization and sentence structure needed attention. She knew, of course, that a few students would simply use AI as a crutch and flaunt the honor code, but such students existed before ChatGPT. For Hilliard, it felt a bit like the Wild West of education, but she found that pulling AI tools into the learning process rather than vilifying them provided opportunities for different kinds of learning and avoided much of the panic that surrounded these new technologies.

What Is It?

Artificial intelligence has long operated behind the scenes in many of the technologies we use every day, but with the introduction of ChatGPT in November 2022, generative AI took the world by storm, capturing the imaginations and stoking the fears of broad swaths of users. Within its first 60 days, the platform grew by 9,900% to reach 100 million users. When the 2023 academic year began, the ChatGPT platform experienced another surge, with 180.5 million unique visitors worldwide during the month of August alone. Artificial intelligence came to dominate conversations and left many searching to understand... what is generative AI?

While the term artificial intelligence has taken on many meanings over the years, generative AI creates content that resembles something produced by humans. The best-known tools create written content, but as the technology has evolved, generative AI tools now can create text, images, sound, or even entire applications. Generative AI is increasingly integrated into various products and services-an estimated 77% of all global companies are either using AI or exploring its use in their business operations. In many cases, these functions are so intricately woven into the fabric of the product that users might not even be aware of their existence. ChatGPT and other generative AI tools are extremely versatile, evolving, and developing into multidimensional tools that are challenging norms and disrupting diverse sectors, including education. An important part of the discussion about generative AI is what it is not. It is not sentient. It lacks awareness and cannot perceive or understand human thought or emotions. The operations are based purely on the data used to train the AI models.

How Does It Work?

Generative AI revolves around neural networks, complex structures inspired by the human brain with interconnected artificial neurons for processing information and learning from data. Whereas algorithm-based models make decisions based on rules that are provided by developers, generative AI tools review large amounts of data to construct an evolving set of rules that guide the generation of new content. Generative AI operates by making predictions about the next item in a series. When generative AI is writing a paragraph, for example, it constructs sentences by predicting each next word in succession. Over time, with lots of practice and lots of data, the system gets better at making predictions.

Large language models (LLMs) are trained on massive textual datasets, including books, articles, and websites, to learn the intricacies of human language. The datasets used to train ChatGPT, for example, were developed using public information on the internet, licensed third-party content, and information supplied by platform users and human trainers. Patterns are identified within the training data through statistical analysis as the machine learning model detects recurring sequences of data, such as words, phrases, or patterns of pixel values in images, and associates them with specific outcomes or contexts. By discovering and modeling these patterns, generative AI can produce new content, ensuring consistency within the context while introducing creativity and novelty. Combining LLMs with natural language processing (NLP) models, which process and understand human language such as language translation and sentiment analysis, results in content that mirrors human-generated material.

Who's Doing It?

The use of generative AI is rapidly growing. Students are increasingly using generative AI tools for essays, research papers, and other written assignments. Faculty members are leveraging generative AI for creating custom learning materials, quizzes, and assessments, and they can also use generative AI to provide timely and accurate feedback to students. Librarians are using generative AI for creating text summaries or discovering insights in their collections. Instructional designers are using generative AI for rapidly iterating on created materials. For administrators, generative AI aids decision-making and communication by summarizing documents and meetings as well as creating documents. Beyond textual tools, generative AI is being used to create visual artifacts and to generate or debug computer code. Software companies are including generative AI into their products like Adobe's Photoshop's Generative Fill or Microsoft's 365 Copilot.

Why Is It Significant?

Generative AI is suitable for a wide range of applications. There are stand-alone applications-such as chatbots, customizable agents, and virtual assistants-in which generative AI is the primary feature. In other applications such as content generators, creative tools, and everyday tools like Microsoft Word and Google Docs, generative AI is essentially invisible and just one feature among many. Part of the reason for this proliferation is that generative AI is becoming open to developers with cloud-based services, open-source frameworks, and a growing number of AI tools that make it easy to implement generative AI solutions.

By allowing individuals and businesses to create, design, and innovate in ways that were previously difficult or expensive, generative AI has the potential to be transformative in many ways, in various professional and educational contexts. Free (or inexpensive) tools that can quickly create content that otherwise requires human skill and time might alter the landscape of what certain jobs look like and the qualifications needed for those roles. In education, the ability for any student to generate written work that is difficult or impossible to discern as having been produced by technology can fundamentally change the kinds of activities that have long been the hallmarks of how students learn and demonstrate mastery of learning material.

What Are the Downsides?

The mismatch in the pace of technological development and the readiness of our social, ethical, and legal frameworks raises concerns about the responsible use of generative AI. It necessitates the development of robust guidelines, policies, and ethical considerations to ensure that this technology is harnessed for the greater good while minimizing potential risks. Training generative AI by using content from the internet means that significant amounts of material that is racist, sexist, or violent must be managed so that it doesn't infect the tool's output. Generative AI's ability to produce human-like content raises the risk that plagiarism and academic dishonesty will become more challenging to detect, necessitating the adoption of advanced detection tools or a shift in policy. This can also be addressed by fostering a culture of academic integrity and educating students about the ethical use of AI tools.

Because these tools can appear to magically provide the correct answers to questions, some users will trust the output without question, even though the tools sometimes generate counterfactual content. This misplaced confidence creates opportunities for those who use technology to do harm, such as through deepfake images or videos. For those inclined to verify the accuracy of generative AI output, the tools do not always cite sources or explain the reasoning behind the generated content. Meanwhile, the output is difficult to cite as a source because it is not repeatable.

Generative AI tools are accepting larger amounts and more types of data, and it can be difficult to know whether the tools are retaining and using data for other purposes. This uncertainty risks exposing data that should not be shared outside of an organization, including personally identifying information and other private data. The ownership of both the data that the tools are trained on and the output is currently being litigated. Several lawsuits are pending against companies for using data without permission. Depending how these cases are resolved, generative AI tools might be required to remove some data or exclude certain datasets, potentially causing the tools to be less accurate. Meanwhile, the output generated by AI tools has been ruled as ineligible for U.S. copyright because it is not authored by a human. However, a U.S. Supreme Court ruling from 1884 could provide a different argument for those who are interacting with generative AI tools as a "copilot" to create new content.

Many of the companies developing generative AI tools have not provided information regarding how the tools are trained and how safety tests are conducted. Each company has its own set of guidelines for what kinds of content are appropriate in terms of ethical, moral, and equity considerations, but this information is often not made clear to the users of these tools. While multiple governments are developing policies and guidelines for detecting AI-generated content and rooting out algorithmic discrimination, this guidance has not yet been finalized.

Another downside is the environmental impact of these tools. The carbon footprint includes training the model, running the model, and operating data centers. It is estimated that creating GPT-3 "consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent, the equivalent of 123 gasoline-powered passenger vehicles driven for one year."

Where Is It Going?

In response to privacy concerns, organizations will begin to purchase or develop their own generative AI tools that do not share data beyond the organization. A college or university could purchase a tool for its faculty, staff, and students that has access to the institution's data but does not share the data beyond the institution or with certain user groups. As generative AI tools are provided with larger, cleaner datasets, larger LLMs will become better at more tasks. Some of these tasks include providing image-to-text services that could be used to make online courses more accessible by describing images used in the course, better online captions for audio and video, and language translations that are more accurate and localized. Several vendors have already integrated generative AI tools into their products to provide students with tutoring and support in areas such as coaching through math problems and the writing process.

Generative AI tools are emerging that will help faculty curate content for their courses. Some of these tools will build a learning module based on the course learning objectives and other prompts. Other tools can be used to develop assessment activities that are linked to the course content and learning objectives. Future developments may include tools that monitor students' performance and suggest adjustments to course content and assessments.

Another expectation is that generative AI tools will be further integrated into many everyday applications, including learning management systems, common productivity applications such as document and spreadsheet tools, and image editors. It will become difficult to use these common applications without AI tools providing suggestions and guidance.

As the workforce changes, how we prepare students will also need to change. We will need to teach students how AI in their fields will impact their work. This includes determining the source of data and understanding how algorithms are currently being used. Then we will need to teach students how to work with AI so that they are able to use these tools as a "copilot," as opposed to being replaced by these tools.

What Are the Implications for Higher Education?

Generative AI promises to give educators the ability to provide deeper learning and a more personalized experience along with reduced workload, but this will require focus on what and how we want students to learn and how they can obtain the tools to thrive in an environment in which AI is ubiquitous. The ability to rapidly generate many versions of material from different viewpoints allows students and faculty to explore ideas in novel ways. For example, an image could be synthesized around a given concept based upon a student's understanding. This image could be compared with images created by other students to discuss what commonalities and differences are present. This is something that is a strength of generative AI and gives all students, not just those with artistic skills, the ability to communicate concepts visually. It is incumbent on institutions to promote AI literacy among students and faculty and to provide information on how to use generative AI tools effectively and ethically, which can improve students' and faculty's experiences and facilitate learning.

For students, feedback is critical to success. Generative AI can assist with many of the techniques of effective feedback, two of which state that feedback should be specific and given on a timely basis. Faculty can use generative AI to rapidly craft specific feedback for students. Faculty can also leverage AI to teach students how to evaluate their own work, which involves them more deeply in the process of learning.

Institutions need to evaluate how students are being taught and evaluated in light of a tool that can generate content. What is the role that an essay plays in demonstrating mastery, and what is an appropriate role for generative AI in creating that essay? What is the role of homework? What constitutes ethical use of generative AI, and how much uniformity does the institution want when it comes to the use of generative AI tools? Significant investments in professional development and support will be needed to help faculty and administrators effectively integrate generative AI into teaching and administrative processes. Meanwhile, the absence of clear policies and guidelines regarding the use of AI in higher education could lead to inconsistencies in its implementation. Educational institutions should develop comprehensive strategies addressing areas including ethical considerations, data privacy, and intellectual property rights to ensure responsible and equitable use of generative AI.

Notes

1. Graham Duggan, "We've Been Using AI for Years - We Just Didn't Know It," CBC Docs, September 19, 2023 (last updated September 28, 2023); "107 Up-to-Date ChatGPT Statistics & User Numbers," Nerdynav, November 27, 2023; Anna Tong, "Exclusive: ChatGPT Traffic Slips Again for Third Month in a Row," Reuters, September 7, 2023.

2. See "Second Request for Reconsideration for Refusal to Register Théâtre D'opéra Spatial," Copyright Review Board, September 5, 2023.

3. See "Burrow-Giles Lithographic Co. v. Sarony," March 17, 1884, Legal Information Institute, Cornell Law School.

Contributors

Heather Brown is Instructional Designer at Tidewater Community College.

Steven Crawford is the District Director for the Maricopa Center for Learning and Innovation at Maricopa County Community College District.

Kate Miffitt is Director for Innovation at California State University, Office of the Chancellor.

Tracy Mendolia-Moore is Manager of 3D Education Technology Innovations at Western University of Health Sciences.

Dean Nevins is Executive Director of Information Technology at Santa Barbara City College.

Joshua Weiss is Director of Digital Learning Solutions at Stanford Graduate School of Education.

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7 things you should know about generative ai

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7 Things You Should Know About Generative AI

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The release of ChatGPT and similar AI tools that generate content including text, images, and audio has prompted both excitement and apprehension amon

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Exploring the Opportunities and Challenges with Generative AI

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Exploring the Opportunities and Challenges with Generative AI

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Effectively integrating generative AI into higher education requires policy development, cross-functional engagement, ethical principles, risk assessments, collaboration with other institutions, and an exploration of diverse use cases.

Credit: Dmitry Rukhlenko / Shutterstock.com © 2024

In October 2023, EDUCAUSE hosted a two-day discussion called "ChatGPT and Generative AI: Navigating Leadership Opportunities and Challenges" with eight higher education expert panelists and more than one hundred higher education professionals. Together, participants identified and discussed various areas of interest: cost, funding, and experimentation; pedagogical opportunities; policy strategies; AI use cases; and leadership strategies and ethical considerations. As the dialogue unfolded, a central theme emerged concerning how higher education institutions can adapt to the rapid pace of technological change, emphasizing the need for responsibility to guide the conversation around AI. Given that the AI space is complex and evolving, participants urged that ongoing guidance is needed. The event generated insights and recommendations that can help the higher education community navigate the rapidly changing and consequential developments around AI.

Cost, Funding, and Experimentation

As generative AI becomes more ubiquitous, and as AI functions are increasingly being added to various technology tools and systems, colleges and universities will need to address issues of cost and funding:

Currently there is a lack of visibility into the costs associated with AI implementation, especially for seemingly free services.

Much more transparency and awareness is needed when budgeting for AI resources.

As AI technologies mature, users will require a better understanding of the direct and indirect costs involved.

The increasing incorporation of AI features into existing software and products raises various concerns for higher education, among them procurement challenges and the importance of staying informed about AI features introduced through updates in widely used software. One approach to balance those concerns against the opportunities in exploring and experimenting with AI technologies is for institutions to procure and enable local, controlled AI environments that are carefully vetted and established for specific uses, especially learning/student uses. Doing so at a time when responsible use and practice are still developing could go a long way toward supporting successful applications.

Pedagogical Opportunities

A key challenge that generative AI tools pose is how to use them effectively for faculty and student development. Faculty members need to understand and critically evaluate AI outputs, and innovation must be balanced with digital literacy. Various approaches, such as integrating AI into coursework and fostering a culture of critique, can help bridge the gap between faculty and technology.

The issue of attribution in the context of AI-generated content raises various ethical considerations. Users-whether they be students, faculty, institutional staff, or others-have a responsibility to acknowledge AI contributions in the creation of products, and using AI in the creative process carries potential impacts on intellectual property and the inclusion of diverse voices.

Policy Strategies

The breadth and extent of the logistical and ethical challenges that generative AI poses for higher education highlight the need for proactive and agile policy development, ongoing dialogue, and collaboration across disciplines. As with many technologies, a particular challenge with policy development around AI is that it's a moving target. The technology is evolving rapidly, and our understanding of its implications is often lagging. Therefore, a practical approach might be to create a set of guiding principles or ethical frameworks that can adapt to changes in technology and emerging ethical concerns.

An exploration of the policy context for AI could include some or all of the following:

Conduct a thorough policy audit. Policy review and development should involve not only crafting new policies but also examining and updating the existing policy library.

Review existing institutional policies that could apply to various uses of AI. Consider the potential risks faced by students and faculty due to the misapplication of policies that were not originally designed with AI in mind, such as a plagiarism policy that predates the ways AI can be used in the writing process.

Establish and engage in cross-functional conversations on campus. Involve students in meaningful ways, and stay abreast of evolving AI technologies (especially those embedded in publicly available tools) to ensure responsible use and equitable integration.

Create task forces or agile groups to explore and track use cases and navigate the complex landscape of AI. This approach parallels the collaborative efforts needed during events such as the COVID-19 pandemic.

Another innovative approach to introducing AI policies could involve integrating AI discussions into broader topics of interest (e.g., academic research and human resources), fostering trust and understanding before delving into the specifics of policy development. This approach aims to engage stakeholders in a meaningful conversation about AI, its implications, and the institutional values guiding its use.

Other steps that institutions could consider when developing AI-related policies or frameworks include the following:

Assess the current landscape (local and beyond). Understand the current uses of AI within the institution. Identify where AI is being used, what types of data are involved, and the impact on students and faculty.

Engage stakeholders. Involve various stakeholders-including faculty, students, technology professionals, staff, and legal experts-in the conversation. Understanding different perspectives is crucial for creating comprehensive policies and guidelines.

Clarify existing policies. Review and clarify existing policies related to data privacy, academic integrity, and ethical conduct. Ensure that AI-related considerations are integrated where necessary. Especially consider crafting an AI policy that is in addition to and different from a plagiarism policy, and develop a handbook to help educate and encourage effective practice.

Understand AI-provider policies. Consider how policies govern the AI coming from or embedded in third-party tools.

Educate the community. Provide education and training on AI technologies for faculty, students, and staff. Help them understand the potential benefits, risks, and ethical considerations associated with AI.

Define ethical principles. Develop a set of ethical principles that guide the use of AI within the institution. Consider dimensions such as transparency, fairness, accountability, and privacy.

Undertake risk assessments. Conduct risk assessments to identify potential risks associated with AI use, and proactively consider how to respond to the risks. For example, some AI contract language allows data provided by students to be used in the development of new products. Policy development should include evaluating the impact on students' well-being, academic integrity, research, and data security.

Plan for iteration. Recognize that AI technologies and ethical considerations are evolving. Establish a framework that allows for continuous iteration and adaptation as technology and ethical norms develop.

Communicate about policies. Clearly and regularly communicate the AI-related policies to the entire community. Ensure that everyone is aware of the guidelines, expectations, and consequences associated with AI use.

Remember to monitor and audit. Implement mechanisms for monitoring and auditing AI applications within the institution. Regularly assess their impact on and adherence to ethical principles.

Collaborate with other institutions. Collaborate with other educational institutions and organizations to share insights, best practices, and collective wisdom while navigating the ethical landscape of AI in education.

Establish protocols for AI tool selection and use. Develop policies and/or guidelines addressing tools that may be used in instruction with regard to a centralized review of privacy, security, and quality.

A recent article in EDUCAUSE Review called "Cross-Campus Approaches to Building a Generative AI Policy" provides an in-depth look at the work of creating policies for generative AI. Meanwhile, check out the community-supported Syllabi Policies for AI Generative Tools for a growing collection of AI policies related to teaching and learning.

AI Use Cases

An EDUCAUSE QuickPoll conducted in April 2023 identified a long list of AI uses, which can be organized into four categories: Dreaming (helping you think), Drudgery (lightening your load), Design (building your content), and Development (advancing your work). AI use cases cover a wide and expanding set of users and functions, such as the following:

Student Advising Assistant: Implement AI to assist with student advising, streamlining the process and potentially enhancing personalized guidance.

Clinical Treatment and Research: Explore AI applications in clinical treatment and research, exploring potential advancements in health care and medical fields.

Literature Assignment: Assign literature students to hold conversations with ChatGPT, embodying characters from books and discussing personal anticipations, thereby demonstrating creative and educational use of AI.

Virtual Teaching Assistant: Leverage AI as a virtual teaching assistant, contributing to educational support and potentially reducing the burden on educators.

Coding and Script Generation: Use AI to generate script and code, indicating applications in programming and content creation.

Video Production and Analysis: Involve AI in video production, including cloning professors, analyzing course survey results, generating visuals for online courses, providing multilingual audio, and testing assignment instructions and grading rubrics.

Topic-Specific Virtual Tutors: Test various language models for efficacy as topic-specific virtual tutors, assessing their pedagogical impact on student learning.

Complex Learning Design Workflows: Manage complex learning design and development workflows using AI, suggesting efficiency improvements in educational content creation.

Accessibility Remediation: Use AI workflows and tools to address holistic accessibility remediation in learning media, ensuring inclusivity.

Online Course Generation: Generate online courses by leveraging AI to compile content from various institutions, indicating potential collaboration in education.

Student Success Analytics: Use AI to generate analytics related to student success, providing insights for educational improvements.

Communication and Change Management Plans: Develop communication and change management plans around upgrades, maintenance, and service launches, emphasizing the importance of strategic implementation.

IT Responsiveness Research: Conduct research and validation on IT responsiveness, disaster recovery, and ransomware preparedness, demonstrating AI's role in enhancing IT security and recovery.

This list of use cases and others collected from AI articles can and should be augmented with a local community's input, organized into categories, and assigned to working groups to explore and track outcomes.

Leadership Strategies and Ethical Considerations

Higher education institutions have an important role to play in shaping the future landscape of AI, and a blog post from Tyton Partners aims to demystify the hype surrounding AI and help institutional leaders know where to focus their energy and actions. One dimension of higher education's role is the potential for collaboration between academic institutions and AI-focused companies, emphasizing the need for tools designed for and aligned with ethical standards. Many AI tools have not been developed with built-in guardrails or warnings concerning the protocols for effective use, and AI tools have the potential to produce misleading or inaccurate content, underscoring the importance of responsible development and usage. A partnership between higher education and AI service providers could support responsible integration, ensuring that AI aligns with the values and goals of an academic community.

Keep in mind that the goal is not merely to set rules but to foster a culture of responsible AI use within higher education and to promote learning. Ethical considerations should be woven into the fabric of education and technology, emphasizing the importance of thoughtful, purposeful, and human-centric AI integration. The focus should be on fostering a mindset that encourages questioning and critical evaluation, ensuring that AI serves as a tool for empowerment rather than as a replacement for human intellect.

What's Next

EDUCAUSE will continue to create resources and convene groups to understand and, in some cases, influence the evolution of generative AI products and services for higher education. The EDUCAUSE Library includes an Artificial Intelligence (AI) topic page that is regularly updated with resources, including the recently developed "7 Things You Should Know About Generative AI." Anyone can join the EDUCAUSE AI Community Group to learn and collaborate with others on AI and AI-related topics. In December 2023, EDUCAUSE held a Member QuickTalk | Drafting an Institutional AI Policy; the resources from the event and a recording of it are available to anyone at an EDUCAUSE member institution. As with any new, potentially transformative technology, the best outcomes for AI will be achieved when community members come together to share information, ideas, concerns, and solutions in an evolving, uncertain space.

Notes

1. Esther Brandon, Lance Eaton, Dana Gavin, and Allison Papini, "Cross-Campus Approaches to Building a Generative AI Policy," EDUCAUSE Review, December 12, 2023.

2. Mark McCormack, "EDUCAUSE QuickPoll Results: Adopting and Adapting to Generative AI in Higher Ed Tech," EDUCAUSE Review, April 17, 2023.

3. Kristen Fox and Catherine Shaw, "Artificial Intelligence in Higher Education: Trick or Treat?" Tyton Partners blog, October 31, 2023.

4. "7 Things You Should Know About Generative AI," EDUCAUSE Review, December 6, 2023.

Veronica Diaz is Senior Director of Professional Learning and Development at EDUCAUSE.

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exploring the opportunities and challenges with generative ai

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Exploring the Opportunities and Challenges with Generative AI

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Effectively integrating generative AI into higher education requires policy development, cross-functional engagement, ethical principles, risk assessm

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Digital Transformation 2.0: The Age of AI

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Digital Transformation 2.0: The Age of AI

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With the rapid developments in artificial intelligence, higher education is entering a second phase of digital transformation: Dx 2.0. Technology and other campus leaders can take steps now to help prepare their institutions.

Credit: Numax3D Creations / Shutterstock.com © 2024

Over the past five years, higher education institutions have been leveraging digital transformation (Dx) to advance their services, streamline operations, and further strategic initiatives. The EDUCAUSE Dx Journey framework has helped IT and other teams across colleges and universities think through the elements needed for success and lasting impact.

Rapid developments in artificial intelligence (AI) represent a significant opportunity to enhance and magnify the effect of Dx projects. AI can act as an accelerant that boosts the value of Dx efforts through its powerful and broad influence on core components of Dx initiatives, ushering in a new chapter of digital transformation on our campuses. Incorporating AI into the Dx framework provides a pathway for institutional leaders as they explore ways to harness the power of AI across the campus.

Key Components of Digital Transformation

In Washington, D.C., in the spring of 2017, EDUCAUSE convened two expert panels of representatives (including myself) from varied colleges and universities to talk about the transformative significance of technology in higher education. Those panel meetings led to the formation, in 2018, of the EDUCAUSE Task Force for Digital Transformation to further explore and refine this work. Our charge was to better understand what digital transformation meant for higher education and the underlying forces that were acting as transformative catalysts for these changes. The work highlighted the evolution of the role of campus IT organizations from service provider to strategic partner.

We grew to understand that the transformative impacts that we were seeing were the result of two sets of factors. The first set was what we called "signals" or "changes" that were occurring outside of our campuses. At the time, these signals included technology advancements such as big data, cloud computing, and universal connectivity, along with other changes such as how people consumed information and interacted with technology.

The second set of factors consisted of a series of intentional shifts that were needed for true transformation to occur. This was about not just shifting the technology but also, and simultaneously, working to achieve systemic shifts in the culture and the workforce of our institutions. Only when sustainable changes were achieved in all three areas would true, long-lasting transformation occur. We illustrated this idea with a "formula":
Intentional Shifts in Technology + Culture + Workforce = Digital Transformation
We used the phrase "Digital Transformation" (Dx for short) because technology (i.e., digital) advances served as a primary catalyst for this transformation, but all three components are needed for success.

Fast forward five years, to today. The basic concepts of digital transformation still apply and in many ways are more relevant than ever. Our campuses continue to look to technology to help advance our missions, streamline operations, and provide critical insights and services for our students. We engage in conversations across the institution to help shape the culture, and we continually assess and redefine the roles, skills, and jobs needed from our workforces for success. True transformations are occurring across our campus communities, as evidenced by the examples in the EDUCAUSE Dx Journey.

Digital Transformation 2.0

The explosion in AI developments over the last year has introduced a third transformative set of factors influencing our Dx efforts and has ushered in the dawn of a new era of digital transformation for higher education. In the coming years, AI tools will power new and deeper transformations of the services that institutions offer, will change how those services are provided, and will affect all areas of the college or university. Campus leaders and IT service providers need to be thinking about those changes now and about the steps that can prepare our institutions for the transformations ahead.

AI changes both sides of our Dx equation. It has the potential to accelerate and multiply the impacts of the technology, culture, and workforce shifts that are needed to achieve digital transformation and also to exponentially increase the positive effects of digital transformation for our institutions, students, faculty, and staff.

In addition, AI acts as a disruptor, requiring that we rethink many aspects of our teaching and learning, institutional operations, campus jobs, and service offerings. It serves as an impetus for change and as an opportunity for reinvention. It raises potential ethical and policy issues that require careful consideration and navigation.

While AI has been around for a while, we are in a period of rapid development and commercialization. Over the past year, much attention has been on public, broad-function large language model (LLM) chatbots such as Open AI's ChatGPT, Google's Bard, and Microsoft's Copilot. Their impact on teaching and learning has required rethinking about student assignments, course curricula, and needed skillsets for tomorrow's workforce. Receiving less attention than these chatbots, but in many ways equally disruptive (if not more), are specific-purpose AI tools and functionalities that are starting to be embedded into everyday workflows and applications. Also of importance are private LLMs that can securely leverage institutional data for AI-informed insights and actions.

Preparing for AI

To achieve the benefits of AI-accelerated Dx 2.0, we should be taking several steps in the areas of technology, culture, and workforce. These steps will ensure that we have the foundations in place to support these new Dx efforts.

Technology

Many of the technology investments we've made to support past transformations will continue to position higher education well for Dx 2.0. But a few additional areas can help us prepare for AI-powered transformations:

Build a robust data ecosystem. It's all about the data. The rapid development of AI tools that provide insights and guidance based on institutional data is one of the key drivers of Dx 2.0 benefits. Having a robust data infrastructure is vital. Invest in data repositories and circulatory systems to securely and appropriately move data and make it available throughout the institution, ensure that you can tap into data collected by your cloud-based and on-premises enterprise solutions, and build out your analytics framework. Another important foundational piece is establishing effective data governance processes and privacy policies and procedures to ensure that data is clean and being accessed and used appropriately by these tools.

Enable universal connectivity. AI relies on access to large datasets and computational power and often utilizes real-time information from sensors and other devices. Ensuring that your campus has universal connectivity is critical: indoor Wi-Fi everywhere, and outdoor Wi-Fi in key locations. Working with cellular providers will supply adequate cellular service across campus. Partnering with local municipalities on rural broadband efforts and other initiatives can bring connectivity to campus constituents no matter where they are located.

Work toward scalable computing resources. Leverage cloud partners and campus computing environments to understand the economics and scalability around your computing resources. The computational requirements and costs for tools based on institutional LLMs and other computationally intensive AI tools must be understood.

Ensure layered support services. To help prepare for Dx 2.0, you should build technology support services both in the central IT organization and in the functional departments. Layered support services staff who understand how the different pieces fit together, know the flow of the data, and assist with business process analysis and re-engineering will be important to the success of these transformation efforts.

Keep abreast of corporate partners' roadmaps. Many companies have announced ways in which they are planning to incorporate AI into their products, services, and platforms. Technology and business owners should work together so that campus leaders can better understand those directions and the potential impacts to the institution and users.

Culture

As is often the case with transformation efforts, achieving shifts in culture can be one of the hardest aspects of the work. Due to the rapid developments in the AI field and given the mysterious nature of many AI tools, moving from a culture of skepticism and hesitation to one of understanding and embracing can be difficult. Some of the following techniques can help:

Be intentional. Changing any type of culture takes purposeful effort. Write out a communications and interaction schedule and strategy. Create deliberate opportunities for conversations about AI and various use cases that will resonate with each audience.

Work to establish a culture of understanding about AI throughout the institution. "AI" is a broad category of technology tools that go beyond ChatGPT and other LLMs, including tools built into existing applications. Helping people understand what AI is and what it encompasses is critical. Encourage faculty and staff to experiment with AI and to hear from one another about capabilities, use cases, concerns, and limitations.

Foster trust, transparency, understanding and partnership. Establish communication pathways, campus partnerships, and opportunities to build shared understanding. These will go a long way to creating a culture that will be open to change and transformation. Be transparent about limitations and current challenges.

Help the campus community understand ethical, legal, privacy, and intellectual property issues around AI. Partner with faculty, institutional counsel, and other stakeholders to develop policies around the use of AI. Set up working groups and tap into existing governance structures to explore these issues and develop frameworks for the institution. This not only will help your institution evolve its policies but also will bring people together and give attention to these issues while socializing them across the organization.

Seek out early adopters and champions, and find ways to showcase their work and how they are using AI. Be sure to include the good, the bad, and the ugly. Full transparency is important and helps others understand use cases and risks. Support the work of early adopters, and ask them to help in engaging with others.

Create opportunities to explore AI use cases that align with your institution's core values. The use of AI tools may not be appropriate in all situations, and each use case needs to align with the institutional philosophy around student engagement and services. For example, does using AI tools to make admission decisions or to reach out to students at academic risk fit with the overall culture and approach for how your institution serves its students?

Workforce

We are just starting to understand how AI will influence the workforce and the jobs that will be needed (or no longer needed) as a result of Dx 2.0 efforts. For now, these steps will help your IT and campus teams prepare:

Build an understanding of how AI may affect the workforce. Have people across the institution work with the AI tools currently available to understand how they function, develop competencies to leverage them, understand their limitations, and be intentional about including the tools in their workflows. Hold "AI Exploration Sessions" that bring together stakeholders and technology in a lab environment. Ask people to do guided experimentation with various AI tools and approaches, then debrief participants on what they learned, and discuss how they can apply the tools to their work.

Think about how jobs and tasks might change. Start looking at every position within a department or organization, and map out ways that the work may change in one, two, and five years as tools evolve. For each position, ask three questions: What tasks might change? How might they change? What new or meaningful work might the person in the position be able to do as a result of the change? For example, within the next twelve months, admissions counselors might be able to use AI for initial reviewing and scoring of applications (the "what"), their work will change in that they will be spending less time reading applications (the "how"), and they will be able to do more targeted outreach and follow-up that they did not have the time for in the past (the "result").

Consider the impact on your organizational structure. If AI can take care of some of the work, how will job functions in your organization change? Will you still need the roles and positions you have today, or can some be repurposed? What different roles will emerge? Again, think in the one-year, two-year, and five-year timeframes. As AI evolves and improves, what might be the effect on your organization? What training, advancement, roles, and career ladder adjustments will be required?

Provide opportunities to enhance skillsets. What skills will your workforce need in order to be able to leverage AI? Some employees will need training in the specific AI tools embedded in applications; others may need to become versed in prompt development for LLMs such as ChatGPT, Copilot, and Bard.

Staff should be able to see AI tools as offering opportunities to shift the focus away from rote or semi-rote tasks to higher-order work and enabling new ways to improve customer service, focus on more strategic efforts, and add value in innovative ways. These advances do not necessarily mean that employees' jobs are going away but, rather, that AI will enhance how they do their work.

The Impact of Digital Transformation 2.0

Dx 2.0 efforts, leveraging the power of AI and shifts in technology, culture, and workforce, have the potential to impact all areas of higher education. Some examples include the following:

The Student Experience. Developments in AI will provide for a more personalized and connected student experience. For example, over the past few years, a number of institutions have deployed chatbots or other similar services to help students find answers to common questions and take care of routine tasks. With further advancements in AI, these services are becoming more encompassing and capable, ultimately streamlining and strengthening students' experiences in navigating the institution and connecting with others.

Teaching and Learning. Personalized learning, AI tutors, customized lesson plans, virtual role-playing, ideation tools, new visualization tools, and other AI-powered developments create opportunities to enhance the teaching and learning experiences for faculty and students both in and out of the classroom.

Campus Operations. AI enables operating efficiencies and frees up staff to focus on delivering higher-level service and higher-order tasks. For example, AI will increasingly be used to read entrance applications and make more consistent preliminary assessments more quickly than is possible today. AI functions that are built into applications can offer insights, allowing staff to dive deeper into their work and services. Tasks that used to require a lot of manual manipulation of data or information will be done more efficiently and effectively.

Experiential Learning. The ability of AI to build realistic simulations and custom scenarios for students will open up opportunities for immersive and experiential learning across all disciplines.

Research. The development of LLMs and generative AI tools is leading to new ways to analyze huge datasets, unstructured data, and natural language information to provide insights and exploration paths that were not available before or were out of reach for many.

AI at Ithaca College

At Ithaca College, we have been exploring how these new AI tools can provide insights and services in ways that were not available to us before.

We've been using machine learning to build a web-based tool that helps first-year students connect with other first-year students who have similar interests. The tool leverages various pieces of information that we've learned about incoming students to suggest possible students with whom they may want to connect. While not super-sophisticated, the tool has been well received, at least as a way to get students over that initial hesitance to make some connections.

We also have developed a proof-of-concept using a private AI tool to provide insights about a student's academic record (program of study, courses taken, grades received, degree requirements satisfied, and future course schedules). Our initial use case was to have an advisor use this service to quickly get a summary of any performance or retention concerns. But we've also found that if we ask the tool to suggest possible career paths based on the student's academic work, it provides very astute observations and suggestions for the student to explore-a benefit that we didn't expect. We foresee making this tool available directly to students for them to be able to explore career options themselves and receive direct feedback on their academic performance.

Another pilot project is giving a different AI service access to a wealth of Ithaca College public information, such as our entire course catalog and program definitions, admission policies and timelines, financial aid specifications, and public rankings, as well as details about the town of Ithaca. Our goal is that prospective students will be able to use this tool to ask questions about Ithaca College and the town of Ithaca and receive specific and relevant information. Our initial testing has been very positive.

These new tools represent Dx 2.0 examples that will transform the experiences of our students and the information and insights to which they have access. To make these transformations happen requires intentional shifts in technology, culture, and workforce.

Moving Dx 2.0 Initiatives Forward

Many Dx 2.0 efforts will leverage AI developments to enhance the transformational impact of technologies already in place. Other efforts will be part of new months- or years-long campus-wide efforts. The following are a few suggestions to get initiatives started:

Create opportunities for conversations to occur across campus. Technology leaders can help start the discussions, but the various stakeholders need to take an active role in understanding the potential influence of AI on their areas. Go on a roadshow to visit business and academic units to provide a baseline understanding of AI and then highlight some use cases in those areas. Encourage further exploration, and establish partnerships on specific next steps.

Work with institutional stakeholders and governing bodies on AI-related policies and guidelines. What's "acceptable use" of AI by students and faculty? How will AI be used to inform or make institutional decisions? Privacy, intellectual property ownership and copyright, bias, access to training data, and other related issues need to be discussed and thought through.

Acknowledge the limitations, hype, and ethical concerns. AI is not a magic solution. Being upfront about its limitations and problems is important to establishing trust and realistic expectations. But AI is also not going away, and those who are able to embrace aspects of Dx 2.0 will have an advantage in the long term.

Focus on three timeframes: one, two and five years out. We're still early in the Dx 2.0 journey. AI capabilities will get better and become more deeply embedded in our solutions and processes over time. Developing an understanding of where things are heading while acknowledging the limitations of today's tools will help you develop a strategy for tomorrow.

Digital Transformation 2.0 builds on the basic tenants of the current Dx model and leverages the rapidly developing AI advances to accelerate transformations and magnify their impact. Maximizing these developments requires a renewed focus on technology, culture, and workforce shifts. Using AI, Dx 2.0 efforts can create positive transformations for our campuses and throughout higher education.

Notes

1. Report from the 2018 EDUCAUSE Task Force on Digital Transformation, EDUCAUSE, November 2018.

2. Dave Weil, "Putting All Three Components of Digital Transformation to Work on Campus," EDUCAUSE Review, May 18, 2021.

David Weil is Vice President, Information Technology & Analytics, at Ithaca College.

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digital transformation 2.0: the age of ai

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Digital Transformation 2.0: The Age of AI

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With the rapid developments in artificial intelligence, higher education is entering a second phase of digital transformation: Dx 2.0. Technology and

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Text-to-Video AI Could Change How We Think

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Text-to-Video AI Could Change How We Think

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Inside Higher Ed | Higher Education News, Events and Jobs

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As economists make forecasts about the impact of generative artificial intelligence on human jobs, educators worry about detecting AI-generated writing. Educators and students alike wonder: How much effort should humans exert in writing now?

More basically, I wonder if prose will continue to dominate human communication and argumentation. Lately, I've considered the possibility that it will not.

Social media and news outlets have been reacting to OpenAI's announcement last month of Sora, the new generative platform that produces realistic videos from simple natural language prompts. Tell Sora, " historical footage from California during the gold rush," for example, and you'll be treated to a clapboard town full of horses, mules and cowboys amongst scrubby hills and a creek. It is visual magic: something from nothing but eight amazing words.

Many, amazed at how far AI has come, jump to speculate about the near-term effects. No doubt Sora and similar platforms will impact creative human jobs. New brands will dominate markets. And new creative and artistic possibilities will emerge.

But from my vantage, generative text-to-video technologies signal other essential changes ahead to human activity, especially in the realms of human thinking, learning, writing and communicating.

More than other generative platforms released to date, these new tools open paths to multimodal, nonlinear thinking and communication that humans have not fully explored. Beyond lines of text, humans now have enhanced abilities to speak in the language of images with relative ease, presenting new ways to structure communications and to understand each other. Text-to-video technologies open uncharted ways for people to generate and share images, text and sound that match their imaginations and rhetorical intentions better than with words alone, creating new possibilities for sharing ideas, analyzing data and evidence, and crafting arguments.

Beyond Sora, other generative and machine-learning AI tools are already assisting humans in their research and in their writing. Generative AI platforms draft literature reviews and produce writing in multiple styles. And that same work can now be initiated and managed through speech-to-text tools. Adding text-to-video capability with these other existing applications requires no stretch of imagination-especially as virtual and augmented reality environments have begun to show the power of three-dimensional data visualization. Humans seeking to communicate complex ideas may be drawn to machines that can help them craft a constellation of video narratives accessed through spatial-virtual reality (VR), augment reality (AR), extended reality(XR)-experiences. In time, communicating complex ideas in this way could become more common than using prose alone. After all, humans are visual animals.

This confluence of technologies will challenge human sensibilities. Writing and thinking may be mediated through images. Images are not just illustrations, but sources of information and meaning, and this new means to create video may complement, enhance and even replace written text.

Linear text will not disappear. But those who gain proficiency in video-generating technology may learn to think more deeply in new directions as they control images in ways that only gifted film directors once could. Their thoughts may be more complex than if they had relied on text alone, as they visualize metaphors and data. And as with everything with generative AI, the timelines for the impact of this technology on human thinking will likely be highly accelerated.

Now is the time to engage imaginations and experiment to find the boundaries for enhanced thinking and creativity that include, but go far beyond, text. Educators need to understand the ways these technologies exclude and include, and the ways they can be misused and misapplied. Generative AI, predictive analytics, and machine learning will not stand alone, but will integrate with other digital technologies and with approaches such as gamification, adaptive learning and collaboration. The long-term implications warrant our attention. Educators who do not begin adapting instruction and content now will be left behind.
Steven K. Johnson, Ph.D., is senior vice president for innovation at National University.

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Text-to-video AI could change how we think (opinion)

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Text-to-video technology could supplant writing as a primary mode of thinking and communication, Steven K. Johnson writes. As economists make forecasts about the impact of generative artificial intelligence on human jobs, educators worry about detecting AI-generated writing. Educators and students alike wonder: How much effort should humans exert in writing now?

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This link will take you to the website Lew Ludwig made for his workshops.