# data[].attributes.attachment_link.title
Syllabi Polices for Generative AI - Repository

# data[].attributes.attachment_link.readable_text
The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when they encounter new types of sources. In what follows, we offer recommendations for citing generative AI, defined as a tool that "can analyze or summarize content from a huge set of information, including web pages, books and other writing available on the internet, and use that data to create original new content" (Weed).

You should

cite a generative AI tool whenever you paraphrase, quote, or incorporate into your own work any content (whether text, image, data, or other) that was created by it

acknowledge all functional uses of the tool (like editing your prose or translating words) in a note, your text, or another suitable location

take care to vet the secondary sources it cites (see example 5 below for more details)

See below for specific examples. And keep in mind: the MLA template of core elements is meant to provide flexibility in citation. So if you find a rationale to modify these recommendations in your own citations, we encourage you to do so. We've opened this post up for commenting, so let us know what you think and how you're using and citing generative AI tools!

Using the MLA Template

Author

We do not recommend treating the AI tool as an author. This recommendation follows the policies developed by various publishers, including the MLA's journal PMLA.

Title of Source

Describe what was generated by the AI tool. This may involve including information about the prompt in the Title of Source element if you have not done so in the text.

Title of Container

Use the Title of Container element to name the AI tool (e.g., ChatGPT).

Version

Name the version of the AI tool as specifically as possible. For example, the examples in this post were developed using ChatGPT 3.5, which assigns a specific date to the version, so the Version element shows this version date.

Publisher

Name the company that made the tool.

Date

Give the date the content was generated.

Location

Give the general URL for the tool.

Example 1: Paraphrasing Text

Passage in Source

Paraphrased in Your Prose

While the green light in The Great Gatsby might be said to chiefly symbolize four main things: optimism, the unattainability of the American dream, greed, and covetousness ("Describe the symbolism"), arguably the most important-the one that ties all four themes together-is greed.

Works-Cited-List Entry

"Describe the symbolism of the green light in the book by F. Scott Fitzgerald" prompt. The Great Gatsby ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

Example 2: Quoting Text

Passage in Source

Quoted in Your Prose

When asked to describe the symbolism of the green light in The Great Gatsby, ChatGPT provided a summary about optimism, the unattainability of the American dream, greed, and covetousness. However, when further prompted to cite the source on which that summary was based, it noted that it lacked "the ability to conduct research or cite sources independently" but that it could "provide a list of scholarly sources related to the symbolism of the green light in The Great Gatsby " ("In 200 words").

Works-Cited-List Entry

"In 200 words, describe the symbolism of the green light in The Great Gatsby" follow-up prompt to list sources. ChatGPT, 13 Feb. version, OpenAI, 9 Mar. 2023, chat.openai.com/chat.

While we've provided fairly detailed descriptions of the prompts above, a more general one (e.g., Symbolism of the green light in The Great Gatsby prompt) could be used, since you are describing something that mimics a conversation, which could have various prompts along the way.

Example 3: Citing Creative Visual Works

If you are incorporating an AI-generated image in your work, you will likely need to create a caption for it following the guidelines in of the MLA Handbook. Use a description of the prompt, followed by the AI tool, version, and date created:

Fig. 1. "Pointillist painting of a sheep in a sunny field of blue flowers" prompt, DALL-E, version 2, OpenAI, 8 Mar. 2023, labs.openai.com/.

You can use this same information if you choose to create a works-cited-list entry instead of including the full citation in the caption (see MLA Handbook, sec. 1.7).

Example 4: Quoting Creative Textual Works

If you ask a generative AI tool to create a work, like a poem, how you cite it will depend on whether you assign a title to it. Let's say, for example, you ask ChatGPT to write a villanelle titled "The Sunflower" that-you guessed it!-describes a sunflower and then quote it in your text. Your works-cited-list entry might look like this:

"The Sunflower" villanelle about a sunflower. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

If you did not title the work, incorporate part of or all of the first line into the description of the work in the Title of Source element:

"Upon the shore . . ." Shakespearean sonnet about seeing the ocean. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

For guidance on using descriptions and text from the work itself in the Title of Source element, see the MLA Handbook, 5.28 and 5.29.

Example 5: Citing Secondary Sources Used by an AI Tool

You should also take care to vet the secondary sources cited by a generative AI tool-with the caveat that AI tools do not always cite sources or, when they do, do not always indicate precisely what a given source has contributed. If you cite an AI summary that includes sources and do not go on to consult those sources yourself, we recommend that you acknowledge secondary sources in your work.

For example, let's say that you ask Bing AI to explain the concept of the political unconscious, citing sources, and it provides the following answer:

Let's say that you then decide to quote from the final sentence. You need to click through to the source listed in the note in order to get more information than just a URL for the source. There, you will read the following:

Now, you can treat Oxford Reference as your source since Bing AI was merely a research conduit to the source (see MLA Handbook 5.34 for more information). If for some reason you want to treat a source cited in a generative AI tool as an indirect source-and you know it is, in fact, the source for the information provided by the AI, follow the guidance in section 6.77 of the MLA Handbook.

Note

1. At the time of writing this post, ChatGPT doesn't have a built-in feature to create a unique URL to the conversation. However, an outside tool like the Chrome extension ShareGPT can generate such a link. If you use that type of outside tool, include the unique URL that the tool generates instead of the general URL.

DALL-E allows users to download the AI-generated images they create or generate a publicly-available URL that leads to an image. If you choose to create a shareable link for an image you generate with DALL-E (or other similar AI image generators), include that unique URL that leads to the image instead of the general URL.

Works Cited

MLA Handbook. 9th ed., Modern Language Association of America, 2021. MLA Handbook Plus, 2021, mlahandbookplus.org/.

Weed, Julie. "Can ChatGPT Plan Your Vacation?" The New York Times, 16 Mar. 2023, www.nytimes.com/2023/03/16/travel/chatgpt-artificial-intelligence-travel-vacation.html.

# data[].attributes.attachment_link.title
How do I cite generative AI in MLA style? | MLA Style Center

# data[].attributes.attachment_link.description
The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when ...

# data[].attributes.headline
Validity over "cheating" | Recommeded reading! 

# data[].attributes.subject
Validity over "cheating" | Recommeded reading! 

# data[].attributes.headline
James Frontier on Accountability/ Assessment Design

# data[].attributes.subject
James Frontier on Accountability/ Assessment Design

# data[].attributes.attachment_link.title
251: Holding Students Accountable in the Age of AI

# data[].attributes.headline
Declining Teacher Trust in Students & What to Do About It 

# data[].attributes.subject
Declining Teacher Trust in Students & What to Do About It 

# data[].attributes.attachment_link.readable_text
What does it mean when we stop reading texts and instead offload that skill to AI? We desperately need another ChatGPT moment outside of text generation to wake people up and let them know how quickly generative technology will impact skills beyond writing. For $5 a month, anyone is now able to summarize and query a PDF using Adobe's AI Assistant. You can already do this with a number of foundation models, along with a slew of purpose-built AI-reading assistants. Who is talking about this? We've been so laser-focused in education on ChatGPT's impact on writing that we've missed generative AI's impact on a multitude of core skills, like reading.

The implications of having an AI-reading assistant that can summarize anything into your language, at your reading level, can have immense benefits for neurodiverse learners and second-language learners. AI reading assistants could play an important role in equity and access to information. However, the uncritical adoption of AI reading tools poses an incredible risk to student's close reading skills.

Last year, I piloted two purpose-built AI reading assistants within my first-year writing courses to see what affordances they might offer students in a controlled reading of a text. I recently published a chapter in Teaching and Generative AI: Pedagogical Possibilities and Productive Tensionsabout this experience using ExplainPaper and SciSpace to augment reading. Students overwhelming loved the tools, but the pilot caused me deep concern. Like so many different tools that use generative AI, I found the reading assistances were focused on speed and task completion over nurturing developing skills or honing existing ones.

This isn't to say there aren't profoundly helpful use cases for the tools beyond students who struggle with reading comprehension and language acquisition. Who wouldn't want the ability to summarize massive volumes of information with accurate and automatic summaries? A lawyer could go through case law at unbelievable speed. So, too, could a researcher on any subject. But is the purpose of reading in these instances simply to arrive at information that fills a brief or a pad a report? What about an overworked adjunct who has to read and comment on 100+ rough drafts? I think you can see how quickly a tool that promises efficiency can challenge the boundaries of an existing practice.

A few months ago I wrote about The AI Influencers Selling Students Learning Shortcuts and I mentioned how apps like Unriddle AI promise to read for a user and are inundating social media with marketing directed toward students. Some of these posts have a million plus views and all use influencers about the age of students to market AI as a time saver. What they don't mention is how often an app like Unriddle misses key facts from a document. When the selling point is speed over process, nuance and accuracy are often left behind.

When we teach reading as a skill, we're asking students to practice more than analytical thinking through close examination of a text-we're inviting them into a conversation with the reader about their ideas. We want maturing readers to engage with the text, not a summary of it, because we know that doing so means those ideas can help shape and mold a student's thinking, challenging their assumptions, and making them argue within themselves over deeply held beliefs they may have about our world. A bespoke generative summary just doesn't do that. If students adopt it as a replacement for close reading, it may rob them of the opportunity to fully engage with an author's ideas.

If we allow an over dependence on AI reading assistants to undermine students' ability and willingness to closely read and engage with texts, the ramifications could ripple across society for generations downstream from our classrooms. Without skilled critical readers, we risk producing minds that are primed to accept neatly summarized information at face value rather than question it, analyze it from multiple angles, and draw their own nuanced conclusions. This hollowing out of one of the cornerstones of higher-order reasoning could severely inhibit innovation, civic discourse, and our collective capacity to thoughtfully navigate complex issues.

Unchecked, the lure of frictionless reading could produce a profoundly atrophied culture of surface-level ideas rather than exploring them in depth. In such a world, I shudder to think how blind obedience to authoritative-seeming AI outputs could allow misinformation and weaponized narratives to proliferate unabated.

I'm a fiction writer by training and spend a great deal of time honing my craft beyond the plot of a short story. I think about how the language sounds, the order of information, and the rhythm, and timing of sentences. If I load up a short story by James Joyce or Flannery O'Connor to an AI reading assistant it will generate a plausibly accurate summary of the sequence of events within the narrative, but asking for an analysis of the craft of the story is a complete failure. More so, asking an AI to change the reading level of a story written for an adult audience for a middle-school grade level removes nuance, style, and distorts artistic intent.

Uncritically adopting an AI to read for you is far more dangerous a threat to knowledge acquisition and learning than using AI to write for you. We need to navigate a path that advocates for ways for students to use this technology ethically to support their learning and not in a manner that will deskill them because it offers the siren song of timesaving through a frictionless experience.

I'm convinced we need to move away from policies allowing for or against AI and instead examine ways to negate the most disruptive parts of uncritical adoption. We should introduce friction into the reading process of a digital text, not to make it more challenging, but to ensure students slow down and process a text. Educators can do this for digital readings through social annotation tools like Hypothesis and Perusall.

Creating active reading assignments where students are required to annotate a text and discuss it with fellow students slows down the process and adds friction to the experience. It also makes the reading a social activity as opposed to an individual one and invites inquiry and debate within the reading process. This added friction isn't there to make the process of reading harder or easier-it exists to ensure students engage with a text and focus on the act of reading for self-discovery and insight over the speedy affordance of using an AI reading assistant to summarize a text for them.

The stakes are ridiculously high when it comes to reading and AI and I wish more people knew about it or had the bandwidth to think about what is gained or lost by generating a summary of a text instead of reading it. Below is how I concluded my article on reading assistants. I hope you will read it and not use a machine to generate a summary.

In conversation with students after the assignment, I asked them some pointed questions about what it might mean for their future learning or careers if they employed such a tool each day. Many were delighted at the prospect of not having to read for school or work, but when I asked them what their response would be if I used a reading assistant to offload the process of reading an assignment they submitted, or if their employer did the same, many students began to put the short term affordances of offloading reading into the greater context of why reading is a foundational skill for our world. Some pushed back at this notion, saying AI is no different than any other tool and why shouldn't use it all the time if it is available. I wish I could tell you that I picked up my favorite book and read passionately from a passage as a response, but in truth, I understand where students are coming from. They weren't reading for enjoyment or pleasure-reading in this context for them was a utilitarian act, a labor, a means to complete an assignment to reach their desired ends-a successful grade. I find that dichotomy present in nearly every conversation about using generative AI in education-learning has been reduced to a problem and here is a technological solution to help solve it. Now that generative AI technologies are commonplace, educators are going to need to fundamentally examine and articulate the importance of human skills and the value they have. Otherwise, many of our students will simply adopt the tools uncritically as solutions and stand-ins for many of the core skills we associate with learning.

# data[].attributes.attachment_link.title
No One is Talking About AI's Impact on Reading

# data[].attributes.attachment_link.description
What does it mean when we stop reading texts and instead offload that skill to AI? We desperately need another ChatGPT moment outside of text generation to wake people up and let them know how quickly generative technology will impact skills beyond writing.

# data[].attributes.headline
Strategies for Teaching Well When Students Have Access to Artificial Intelligence

# data[].attributes.subject
Strategies for Teaching Well When Students Have Access to Artificial Intelligence

# data[].attributes.attachment_link.provider_name
Stearns Center for Teaching and Learning

# data[].attributes.attachment_link.readable_text
Stearns Center Recommendations:
Strategies for Teaching Well When Students Have Access to Artificial Intelligence (AI) Generation Tools
Overview
Recent advances in Generative-AI tools have resulted in broad worldwide access to "chatbots" and other online sites that can produce extended pieces of writing, artwork, and/or code, some at the level of mid-range college student competencies, in response to a very wide range of topics or questions. This technology is not perfect, but it is also not going away, and it will continue to directly impact how we teach and how students learn. Moreover, the tools, their uses, and their effects on teaching and learning (and professional responsibilities) will continue to evolve. Your students currently have access to these tools, and the few who don't know about them yet will know soon.

It's important to view Generative-AI programs as tools that can be used for a range of purposes, including activities that support learning as well as interfere with learning. You might think, and ask students to think, about how similar tools have changed the work of your field and thus the skills and abilities that educated professionals need, over the past decades:

Multifunction calculators

Spelling and grammar checkers

Website generators

Statistical analysis software

2D and 3D design software

High-powered imaging tools

The arguments in your field for "doing something by hand " vs. using a tool that enhances some individual abilities - both in a learning situation and in a professional context - are likely to be relevant to conversations about AI text generators.

Want to talk more? Register for one of our Fall 2023 AI in the Classroom seminars or learning communities via our Event Calendar.

Communicate Your Course Policies About Gen-AI
Basic Academic Integrity Policy Statements
All faculty should identify one or more recommended statements to use regarding academic integrity and Generative-AI programs. These should be designed to complement or amplify a broad statement about general academic integrity. Your statement may, and likely should, vary depending on the course. Departments, academic units, or programs are encouraged to help faculty create consistent and relevant statements.

Basic Academic Integrity Statement sample:

Mason is an Honor Code university; please see the Office for Academic Integrity for a full description of the code and the honor committee process. Three fundamental principles to follow at all times are that: (1) all work submitted be your own, as defined by the assignment; (2) when you use the work, the words, or the ideas of others, including fellow students or online sites, you give full credit through accurate citations; and (3) if you are uncertain about the ground rules on a particular assignment or exam, ask for clarification. No grade is important enough to justify academic misconduct.

Generative-AI addendum, baseline: Use of Generative-AI tools should follow the fundamental principles of the Honor Code.

Generative-AI addendum, expanded: Use of Generative-AI tools should be used following the fundamental principles of the Honor Code. This includes being honest about the use of these tools for submitted work and including citations when using the work of others, whether individual people or Generative-AI tools.
Generating a more complete and accurate explanation of your goals and policies will take more time.
Since use of Generative-AI tools is likely to vary across disciplines, programs, courses, and even assignments, Stearns Center strongly recommends that faculty provide an addendum that fully articulates for students their policy about using tools like ChatGPT in their course. Academic units can also provide important leadership in this area. However, a one-size-fits-all statement is inappropriate.

Faculty and leaders in academic units may use the chart below to identify language that should be used. Individual faculty should consult their academic unit or program leadership as they compose statements for their courses.

REQUIRED: Core policy language that explains the instructor's or program's particular stance on the use of Generative AI.

RECOMMENDED: Transparency statement that identifies faculty's use of related tools to evaluate student work

OPTIONAL: Expanded language on reasoning

OPTIONAL: Expanded language on discipline or course

OPTIONAL: Expanded language on ethics & access

Please adapt these statements to the particular needs of a field, program, or course. See the next section for more information on communicating about the overall ethics and accessibility of Generative-AI tools.

For more suggestions about course policies, we recommend Dr. Lance Eaton's evolving crowd-sourced collection of sample policies here.

No use

All work submitted in this course must be your own original work; use of AI writing tools, such as ChatGPT, are prohibited in this course and will be considered a violation of academic integrity. All academic integrity violations will be reported to the office of Academic Integrity.

Student work may be analyzed using an originality detection tool focused on Generative AI tools.

Original work is required in this course to meet its learning objectives. Work produced by Generative AI is not original work and will not aid in the learning process for this course.
OR
The learning objectives for this course require students to demonstrate skills and practices that they can reliably perform without supplemental tools.

In order to do the higher-level work in X field, you need to understand/apply fundamental concepts and skills without the use of AI tools. Practicing without the use of these tools will aid in learning.

Some use

When explicitly stated by the instructor, Generative AI tools are allowed on the named assignment. Students will be directed if and when citation or statement-of-usage direction is required. Use of these tools on any assignment not specified will be considered a violation of the academic integrity policy. All academic integrity violations will be reported to the office of Academic Integrity.

Some student work may be analyzed using an originality detection tool focused on AI tools. Generative AI detection tool use will be revealed when the assignment directions are provided to students.

Use of Generative AI tools will sometimes be in alignment with the learning outcomes for this course; when meeting the outcome requires original human action, creativity or knowledge, AI tool use would not align with the stated course goals.

There will be times in X field that use of AI tools will be needed for you to do well at the job and there will be times where you will need to be able to do the work without support from these tools. This course aims to provide you with experience in the real-world scenarios that you may encounter once you leave the university.

Unlimited use

Students may use Generative AI tools whenever they believe it would be useful to their learning of course material. Students will be directed if and when citation or a statement-of-usage is required. All academic integrity violations will be reported to the office of Academic Integrity.

Student work will not be submitted through originality detection software focused on AI tools because the utilization of these types of tools is expected.

Although you are unrestricted with your use of Generative AI tools, you will be responsible for any incorrect, biased, or unethical information that is submitted, and your assignment grade will reflect the inclusion of any material that is incorrect or offensive.
AND/OR
Additionally, you must be transparent with your use and identify when the tool was used.

Use of these tools will likely be very commonplace in X field and gaining experience in the ethical and efficient use of these tools will help you in your professional life once you have left the university.

Required use

This course will require the use of Generative AI tools for some (or all) assignments and assessments. Therefore, the use of these tools when instructed will not be considered a violation of academic integrity. Students will be directed if and when citation or a statement-of-usage is needed. Non-directed or unethical use may be a violation of Academic Integrity and so be reported to the office of Academic Integrity.

Student work will not be submitted through detection software because the utilization of these types of tools is expected.
OR
Detection software may be used for assignments where using Generative AI tools does not align with the course learning objectives.

Work produced with the aid of Generative AI is not without risk. You will be responsible for any incorrect, biased, or unethical information that is submitted and you must be transparent with your use even on assignments where you are required to use Generative AI.

The use of Generative AI will be a crucial set of skills that you will need to have a successful career in this field.
AND/OR
Entry level jobs in X field will require sufficient knowledge and experience with prompt engineering [or other AI skills that are relevant to your field I.e., coding].

Generative-AI Detection Tools and Other Ethical Issues
"Detection" Tools
Stearns Center recommends skepticism about tools or programs that indicate a reliable way of detecting whether text, artwork, or code is fully or partially generated by an AI tool.

Problems with detection technology: Note that there are some technologies existing and more on the way that will claim to be able to "detect" writing from an AI text generator, but using them remains problematic in many ways:

These may provide some feedback to instructors, but they are unlikely to be anywhere near a foolproof tool. (See this video of a faculty member experimenting with a flawed detector as just one example among many.)

For a thorough explanation of the principled reasons and technical limitations that make relying on any kind of "AI detection" tool problematic, see University of Rochester professor Whitney Gegg-Harrison's overview here.

Problems with detection as a core teaching approach: Chasing after "cheating" students, particularly as technologies continue to develop additional capabilities, is a losing game. Stearns Center continues to recommend that faculty invest their time now in alterations to their courses, messaging, assignments, and support structures to encourage and enable students to engage in meaningful assessments of their learning.

In addition, we recommend that faculty generally avoid actions that over-prioritize "catching/preventing cheating" while placing undue stresses on equity, accessibility, and innovative thinking:

Requiring handwritten submissions places additional burdens on students, especially those with some physical disabilities

Adopting intensive surveillance tools or processes - such as requiring all steps of writing to be done under human and technological supervision - is likely to produce few gains while limiting students' thinking and introducing additional biases and inequities into the learning process

Planning out systems to personally "recognize" "original" writing, either through eyeballing and comparing in-class samples to take-home samples by the same student, or by using an external text-evaluation tool, are likely to cause more problems than solutions, since all of these approaches are subject to bias and error that may reduce equity without significant gains in integrity

Concerns & Limitations Regarding Ethics and Access

Departments, academic units, or programs can help clarify how faculty should or may communicate about the challenges inherent in course-use of Generative-AI tools. For instance, instructors should consider both access and privacyissues:

While there are some free Generative AI programs, some do require paid subscription for access. Assignments should be designed to ensure equal access for all students.

Use of some Generative AI programs requires accounts, and these programs may track or retain student input. Some students may not wish to create an account using their personal information, or to submit their original work to a Generative-AI program. Faculty should provide alternatives for students who may not wish to create personal accounts.

Sample statements to students (and/or faculty) about limitations, concerns, ethical quandaries posed by Generative-AI tools.

Generative AI tools have limitations: they currently have the tendency to hallucinate (create incorrect statements and provide fake citations), create inaccurate code, and provide offensive images or examples. They have been trained on limited sources that may contain biases and create biased output. The use of these tools creates other ethical quandaries: the algorithms rely on work done by other humans and do not give credit to their sources, and the algorithm adjustments are often completed by workers who may not be fully compensated for their labor and emotional stress. Lastly, reliance on these tools may stifle your own creativity and impede the learning process. Your decisions to use Generative-AI tools should always take these limitations into account.

Currently there are some unknowns about who owns the right to the materials used with Generative AI tools - including original student work that is submitted to a Generative-AI program. If you do not wish to risk (or give up) the rights to your intellectual property, you should consult with your instructor.

Model Activities

See below for a list of assignment examples faculty can integrate into their classrooms:

General Teaching Suggestions

For your current courses, you should take some basic steps. Additional resources and examples follow this list.

Learn: Familiarize yourself with the kinds of text that AI tools can generate.
[: We recommend not using language that suggests that AI tools "compose" or "write" texts; they are algorithms assembling words and producing text in using a prescribed data set.]

See a brief explanation of how AI text-generation works here or here.

See examples of AI-produced text in response to a prompt here and here (content disclaimer: the second link is a public social media thread with examples).

Try an AI text-generation tool out for yourself here or here.

Read about the limitations of "AI Text Detection" tools here and here.

Consider some of the implications for teaching: There's a great short IHE article here.

Consider adapting some of your assignments to increase the value of tasks where students do the work without AI assistance and to limit the opportunity for AI assistance to meaningfully contribute to student success.

Increase the specificity of your question or task: For example, rather than an open-ended writing prompt ("compare X and Y"), ask for specific information or examples (ask students to expand on one point discussed in class, create a scenario and ask for specific resources/sources, or provide an extended reading and have students critique/analyze it)

Require the application of key concepts to specific local cases, problems, or contexts: For example, provide students with a reading and ask them to analyze and expand on the topics or issues in order to focus the students' thoughts and responses on that paper, example, or case study.

Require inclusion, citation, and direct response to specific course texts: For example, prompt students to cite the textbook or another reading in class, in addition to X number of additional sources that they find in Y database in your field.

Require proposals, drafts, and revisions. Multiple, sequential stages of a larger assignment can be useful to help students scaffold their understanding as well as help them produce their own content. For example, credit/noncredit activities can be integrated for topic development (and you can provide time in class on this or with paired peer activities). Low-stakes activities can be useful for writing out outlines of papers (for grading, these can be completion/noncompletion or similar). Peer reviews and iterative development, particularly when combined with a short (one-paragraph) reflection on what changes were made as a result of a first submission or peer review help students continue to frame and develop their learning.

Shift grading criteria, especially for formative writing such as discussion boards or drafts, to place additional value on how students respond to and/or integrate ideas or feedback from their peers or from you. For example, integrating more specifics in discussion prompts (see above), then having students make peer comments and respond to each other, will help continue the conversation and allow students to develop their thoughts on the topic more fully.

Consider adopting activities that include AI text generation as a sample, starting point, and/or opportunity for critique-and-revision

Continue learning about AI text generation in higher education settings
Begin to explore what "competency" will mean in your field/profession: In 2025 or 2030, as AI tools continue to expand, what do you imagine will separate "average" professionals in your field from leaders in the field? What will experts be expected to have memorized, to do "by hand" without tools, to do quickly in one try? What kinds of complex problems will they need to address using all the tools available? Mason students should be learning (and being assessed on) high-value skills.

Consider upcoming issues for educators and students

Individually, within your academic unit, and/or with colleagues participating in Stearns Center events, keep exploring the larger questions raised by these new tools, such as:

What is "original" student work, and when do we most want it, and for what (educational/growth/ethical) purposes?

What will students need to do to be "good writers" as educated professionals, as AI text generators continues to gain facility in generating accurate and relevant text?

How are current AI text generators in a "free" mode likely to progress toward a for-profit approach, and how will that affect students and young professionals?

How can AI text generators be used to support inclusion and equity in higher education?

How can responsible writers incorporate the use of AI text generators going forward? For instance, would giving citations and building further or editing the content be sufficient? Are there any "baseline" uses for composing early drafts?

How can faculty shift their use and assessment of student writing to continue to encourage students to use writing for learning, communication, and documentation of their thinking?

What kinds of privacy and accessibility concerns might be raised concerning AI text generators?

What are the ways that bias and various "-isms" (for example, racism, sexism) appear in AI generated texts? How do educators consider the potentials of hate speech?

Additional resource pages for faculty

Stanford faculty weigh in on ChatGPT's shake-up in education. Stanford Graduate School of Education. December 20, 2022. https://ed.stanford.edu/news/stanford-faculty-weigh-new-ai-chatbot-s-shake-learning-and-teaching

Five chats to help you understand ChatGPT. The Atlantic. December 8, 2022. https://www.theatlantic.com/technology/archive/2022/12/openai-chatgpt-chatbot-messages/672411/

Robo-writers: the rise and risks of language-generating AI. Nature. March 3, 2021. https://www.nature.com/articles/d41586-021-00530-0

AI Will Augment, Not Replace. Inside Higher Ed. December 14, 2022 https://www.insidehighered.com/blogs/just-visiting/guest-post-ai-will-augment-not-replace

AI and the Future of Undergraduate Writing. Chronicle of Higher Education. December 13, 2022 https://www.chronicle.com/article/ai-and-the-future-of-undergraduate-writing

Hidden biases and societal risks from People of Color in Tech, Christian Ilube, Dec. 13, 2022

"On the Dangers of Stochastic Parrots" by Emily M. Bender, Timnit Gebru, et al, FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 2021. Association for Computing Machinery, doi: 10.1145/3442188.

Additional resources will be added to this page as they become available.

# data[].attributes.attachment_link.title
AI Text Generators: Resource Page - Stearns Center for Teaching and Learning

# data[].attributes.attachment_link.description
Stearns Center Recommendations:Strategies for Teaching Well When Students Have Access to Artificial Intelligence (AI) Generation Tools Overview Recent advances in Generative-AI tools have resulted in [...]

# data[].attributes.attachment_link.title
State AI Guidance for Education — AI for Education

# data[].attributes.attachment_link.description
25 states and their departments of education now have official guidance or policy on the use of AI in schools. We’ve compiled them in this handy resource.

# data[].attributes.headline
Why AI Detection Alone Will Fail Students

# data[].attributes.subject
Why AI Detection Alone Will Fail Students

# data[].attributes.attachment_link.description
Why AI Detection Alone Will Fail Students

# data[].attributes.headline
Exemplar: Essential Agreements for Assessment

# data[].attributes.subject
Exemplar: Essential Agreements for Assessment

# data[].attributes.body
Created by myself and Richard Kent (informed by the IB Programme Standards and Practices), this is the kind of framework that continues to support effective assessment practice and mitigate academic integrity issues, even in the age of AI.

# data[].attributes.headline
Basic Tips for Course Design/ Assessment Design

# data[].attributes.subject
Basic Tips for Course Design/ Assessment Design

# data[].attributes.attachment_link.readable_text
Seemingly every year, there is a new platform or website that prompts us to think about how we are assessing student learning and how we can ensure that students are demonstrating what they know with integrity. What follows are several strategies for promoting academic integrity, as well as some specific information on Artificial Intelligence tools (or AI tools, e.g., ChatGPT) that may help inform how you assess learning and create a learning environment that fosters trust and academic integrity.

Strategy #1: Design Opportunities for Authentic Assessment

Perhaps the most effective strategy for ensuring academic integrity involves authentic assessment-tasks that call for students to demonstrate the skills and knowledge they have learned in a real-world (or close to real, authentic) context. Authentic assessment requires students to transfer knowledge, often by engaging in higher order thinking skills (see Bloom's Taxonomy). The extent to which assignments call for the demonstration of learning in authentic, creative, and/or personalized ways minimizes students' ability to outsource academic work. This might involve having students identify themes in various course readings, class discussions, learning experiences (e.g., a lab, service-project), or their personal lived experience. Furthermore, designing maximally authentic assignments is an evidence-based practice that will help students engage more meaningfully with course content.

For more on assignment design with an awareness of AI bots (e.g., ChatGPT), please see the specific information included below.

Strategy #2: Structure Assignments to Lower Grade Anxiety

The following strategies can lower anxiety, which drives many students to consider cheating.

Build structured flexibility into due dates (e.g., by offering a set number of late submissions)

Allow more time for major assignments (whether as a general rule or by individual request)

Scaffold major assignments, establishing incremental check-ins or due dates to ensure students are making adequate progress

Redistribute the relative weight of major assessments, to reduce the stakes of any one assignment

Strategy #3: Communicate Why Academic Integrity Matters

Have clear expectations and take time to explicitly and repeatedly highlight relevant sections of your syllabus (e.g., descriptions of assignments, sources of academic support, flexible due dates, appropriate use of AI tools, relative weights of assignments, etc.), the University's commitment to academic integrity, and your own commitment to supporting students. For additional guidance and sample syllabus language, visit our Canvas site, " Create a Student-Centered Syllabus" under the "Academic Freedom" tab in " Course Management and Policies."

Make sure students are aware of Cornell's Code of Academic Integrity. You may also wish to include an academic integrity affirmation (e.g., test item, statement) that students complete on your syllabus or individual assignments.

Explicitly ask students to honor the Code of Academic Integrity. Here are some sample appeals you can modify for your needs:

Logical appeal

Emotional (moral) appeal

Personal appeal

You may also want to make students aware of the role that academic freedom plays alongside academic integrity in terms of building trust and respect in the classroom. Communicate that each person in class is expected to respect the principles of academic freedom for instructors and classmates and will maintain the privacy of the classroom environment, as outlined in

Let students know that you are but one of many resources on campus that are available to help with their specific challenges. This could even include creating a brief assignment to promote students' familiarity with the supports available to them. Knowing that you are aware and that you appreciate how challenging the undergraduate experience can be may encourage students to come to you before compromising their integrity.

# data[].attributes.attachment_link.title
Promoting Academic Integrity in Your Course | Center for Teaching Innovation

# data[].attributes.attachment_link.description
Seemingly every year, there is a new platform or website that prompts us to think about how we are assessing student learning and how we can ensure that students are demonstrating what they know with integrity. What follows are several strategies for promoting academic integrity, as well as some specific information on Artificial Intelligence tools (or AI tools, e.g., ChatGPT) that may help inform how you assess learning and create a learning environment that fosters trust and academic integrity.

# data[].attributes.attachment_link.readable_text
In the final chapter of Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity (2021) I contemplate the future of plagiarism and academic integrity. I introduced the idea of life in a postplagiarism world; thinking about the impact of artificial intelligence on writing. Here, I expand on those ideas. These 6 tenets characterize the post-plagiarism age:

Hybrid Human-AI Writing Will Become Normal

Hybrid writing, co-created by human and artificial intelligence together is becoming prevalent. Soon it will be the norm. Trying to determine where the human ends and where the artificial intelligence begins is pointless and futile.

Human Creativity is Enhanced

Human creativity is enhanced, not threatened by artificial intelligence. Humans can be inspired and inspire others. Humans may even be inspired by artificial intelligence, but our ability to imagine, inspire, and create remains boundless and inexhaustible.

Language Barriers Disappear

One's first language will begin to matter less and less as tools become available for humans to understand each other in countless languages.

Humans can Relinquish Control, but not Responsibility

Humans can retain control over what they write, but they can also relinquish control to artificial intelligence tools if they choose. Although humans can relinquish control, they do not relinquish responsibility for what is written. Humans can - and must - remain accountable for fact-checking, verification procedures, and truth-telling. Humans are also responsible for how AI-tools are developed.

Attribution Remains Important

It always has been, and always will be, appropriate and desirable to appreciate, admire, and respect our teachers, mentors, and guides. Humans learn in community with one another, even when they are learning alone. Citing, referencing, and attribution remain important skills.

Historical Definitions of Plagiarism No Longer Apply

Historical definitions of plagiarism will not be rewritten because of artificial intelligence; they will be transcended. Policy definitions can - and must - adapt.

Translations of the Six Tenets of Postplagiarism

Since I first shared the Six Tenets of Postplagiarism, the infographic has been translated into French and Spanish. I am very grateful to the translators, who put in time and effort to go through the points in detail. Here are the translated versions:

Les 6 Principes du Postplagiat (French)

This translation was done by Dr. Elaine Beaulieu at the University of Ottawa, Canada.

Colleagues at the Université du Québec à Montréal (Canada), also wrote a web article in French. It is available here: https://collimateur.uqam.ca/collimateur/6-principes-de-lapres-plagiat-a-lere-de-lia/

6 Principios del Posplagio (Spanish)

The Spanish translation was done by Beatriz Moya, PhD candidate at the University of Calgary.

More Resources on PostPlagiarism

Check out my video on this topic on YouTube: https://youtu.be/NxFMMw1QZX0

Check out my article in University World News on this topic: "Artificial intelligence and academic integrity, post-plagiarism"

_________________________________
Share this: 6 Tenets of Postplagiarism: Writing in the Age of Artificial Intelligence https://drsaraheaton.wordpress.com/2023/02/25/6-tenets-of-postplagiarism-writing-in-the-age-of-artificial-intelligence/
This blog has had over 3 million views thanks to readers like you. If you enjoyed this post, please "like" it or share it on social media. Thanks!

Sarah Elaine Eaton, PhD, is a faculty member in the Werklund School of Education, and the Educational Leader in Residence, Academic Integrity, University of Calgary, Canada. Opinions are my own and do not represent those of the University of Calgary.

# data[].attributes.attachment_link.title
6 Tenets of Postplagiarism: Writing in the Age of Artificial Intelligence

# data[].attributes.attachment_link.description
In the final chapter of Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity (2021) I contemplate the future of plagiarism and academic integrity. I introduced the idea of life in a postplagiarism world. Here I expand on those ideas.

# data[].attributes.attachment_link.title
Will the Humanities Survive Artificial Intelligence?

# data[].attributes.attachment_link.description
Maybe not as we’ve known them. But, in the ruins of the old curriculum, something vital is stirring.

# data[].attributes.headline
Core principles for rethinking assessment in the age of AI

# data[].attributes.subject
Core principles for rethinking assessment in the age of AI

# data[].attributes.attachment_link.title
Five Principles for Rethinking Assessment with Gen AI

# data[].attributes.attachment_link.description
These 5 Principles for rethinking assessment were important before GenAI, but now they’re even more relevant. In this article, I explore why validity, authenticity, transparency and trust are key to assessment.

# data[].attributes.headline
Because anything featuring otters goes on the list. Always. 

# data[].attributes.subject
Because anything featuring otters goes on the list. Always. 

# data[].attributes.attachment_link.description
Three years of progress as shown by marine mammals

# data[].attributes.headline
One of U of Chicago's series on AI and Academic Integrity

# data[].attributes.subject
One of U of Chicago's series on AI and Academic Integrity

# data[].attributes.attachment_link.readable_text
Author's Note: This is the latest installment in an ongoing series of articles about issues pertaining to academic integrity in higher education. For earlier installments, please see: Part 1 | Part 2 | Part 3 | Part 4 | Part 5

ATS instructional designers Mohammad Ahmed and Michael Hernandez contributed content to this article.

Introduction

There are few current issues in education that have provoked more interest - or sounded more alarms - than artificial intelligence (AI) technology. While the issue has simmered for some time, it burst into the forefront of debate following OpenAI's public release of ChatGPT. When given a prompt - e.g. "What were the causes of World War I?" or "How does the Krebs cycle work?" - ChatGPT (the acronym stands for "Generative Pretrained Transformer") can generate text that reads, at least on superficial examination, like that written by a human - the basis of the famed Turing Test for machine intelligence.

Once the tool's capabilities became known, it did not take long for fears to be voiced that students would turn to ChatGPT to write their assignments for them. Eye-grabbing headlines began to appear, not only in sensationalist newspapers like the New York Post ( which dubbed the tool "CheatGPT") but in more sober publications like the Atlantic, where an opinion piece bluntly claimed that "the college essay is dead". Advocates for the worst-case scenario see a future in which human-generated and computer-generated text are indistinguishable, essay assignments are meaningless, and the very skill of academic writing is lost.

One need not accept this doomsday proposition to recognize that ChatGPT raises legitimate concerns for academic integrity. But if we are to address such concerns, we must first answer several key questions: what is ChatGPT, exactly? What are its affordances and limitations? And, assuming that ChatGPT and tools like it are here to stay (as seems overwhelmingly likely), how should we rethink pedagogy to address this new reality?

The State of the Tool

At this time, ChatGPT is essentially an information aggregator. It trawls vast quantities of human-produced texts and extracts data, which it then synthesizes into a response to a given prompt. As noted above, its responses on many topics are at least coherent enough that they may be superficially indistinguishable from student writing.

As with all AI tools, though, ChatGPT's capacity to give responses depends upon what, and how much, it is "fed". Its lack of data on current events, for example, limits its capacity to respond to prompts such as "How is the war in Ukraine progressing today?" There are also guardrails in place to prevent the tool's being used for nefarious purposes (though cybercriminals are already seeking to circumvent these).

There are other significant limitations to the tool as well. It cannot cite sources correctly - any request for a bibliography produces false and/or irrelevant citations. Nor is it error-free: users have run across blatant, even comical, mistakes when ChatGPT is asked a question as straightforward as "How do you work?" Like all AI, it is subject to the biases of those who supply its data. And, most fundamentally, it is not true artificial intelligence. There are no indications that ChatGPT understands the questions it is being asked or what it is producing in answer; simply put, it is not sapient. This is worth keeping in mind as the debate rages over whether such tools are capable of supplanting human creativity.

Issues for Academic Integrity

It is, without question, too early in the game to expound upon all the possible difficulties ChatGPT and similar generative AI tools could pose for academic integrity. Already, however, ChatGPT-generated text has proved itself capable of evading plagiarism checkers such as Turnitin. Plagiarism detection software relies on comparing student work to a database of pre-existing work and identifying identical phrases, sentences, etc. to produce an "originality score". Because the text generated by ChatGPT is (in some sense, anyway) "original," it renders this technique useless.

ChatGPT also ties into the broader issue of contract cheating - hiring a third party to do work, such as writing an essay or taking an exam, on a student's behalf. Contract cheating is already a severe problem worldwide, and with the widespread availability of AI writing tools, students can now generate "original" written work for free, without the need to involve a human agent who might betray the student's confidence.

How Do We Deal with the Problem?

As the New York Times has noted, many faculty and instructors already feel the need to adjust their pedagogy to account for the existence of ChatGPT. Their strategies, actual and proposed, for coping with the tool can be divided into three categories: technological prevention; non-technological prevention; and creative adaptation. We shall consider each of these in turn below.

Technological Prevention

It will come as no surprise that technological counters to ChatGPT are already in play. A 22-year-old computer science student at Princeton named Edward Tian has introduced GPTZero, which claims to distinguish human- and computer-generated text with a high degree of accuracy. Meanwhile, other plagiarism tools, such as Turnitin, offer their own AI-detection tools and are rapidly working to detect the newest generation of generative AI text. And finally, the makers of ChatGPT are themselves exploring "watermarking" technology to indicate when a document has been generated by the software.

Some experts foresee an "arms race" between AI writing tools and AI detection tools. If this scenario comes to pass, faculty and instructors will be hard-pressed to keep up with the bleeding-edge software needed to counter the newest writing tools. But more fundamentally, we might ask: is technology always the best solution to the problems it creates? Or are there other, perhaps less involved, means of addressing the questions raised by AI?

Non-Technological Prevention

At the other end of the spectrum, some faculty and instructors have sought to neutralize ChatGPT entirely. This may entail banning ChatGPT specifically; banning all computers in the classroom; supervising student essay-writing, whether in class or via monitoring software such as Proctorio; or even requiring writing assignments to be handwritten.

The concern that underlies such measures is understandable, and they can be effective in the short-term, but they come at a cost: aside from the anxiety that can be provoked by being under surveillance, accessibility issues that may be raised by requiring handwritten work, and the legal/ethical issues raised by video proctoring, students miss the opportunity to learn about the tool and its implications. As we confront the likelihood of a future with ubiquitous AI technology, those students who have never experienced tools like ChatGPT and who know nothing about their uses may well find themselves at a professional disadvantage.

Creative Adaptation

At this point in time, it seems most productive to take a third path - one that balances the need to safeguard academic integrity with the reality that ChatGPT and its like are here to stay. Here are some suggestions for methods to integrate AI tools like ChatGPT into your pedagogy in a productive, rather than destructive, fashion.

Clarify expectations at the outset. As early in your course as possible - ideally within the syllabus itself - you should specify whether, and under what circumstances, the use of AI tools is permissible. It may help to think of ChatGPT as similar to peer assistance, group work, or outside tutoring: in all these cases, your students should understand where the boundaries lie, when help is permissible, and when they must rely on their own resources. You might also discuss with your students how they feel about AI and its ability (or lack thereof) to convey their ideas. Emergent research suggests that at least some students feel dissatisfied with the results when they entrust expression of their ideas to AI.

Craft writing prompts that require creative thought. A tool like ChatGPT can easily respond to a simple prompt such as "What are the causes of inflation?", but it is likely to have trouble with a prompt such as "Compare and contrast inflation in the present-day American economy with that in the late 1970s". The more in-depth and thought out the prompt, the more it will demand critical reasoning - not simply regurgitation - to answer.

Run your prompts through ChatGPT. Related to the point above, actually using ChatGPT on a draft of your writing prompt can be an illuminating exercise. Successive iterations may help you to clarify your thinking and add nuances to your prompt that were not present in the initial draft.

Scaffold your writing assignments. This is a time-honored technique for combating plagiarism of any kind in academic writing. It will be much harder for a student to submit a final draft generated by AI and get away with it if you have observed that student's thinking and writing process throughout the course.

Promote library resources. As mentioned previously, ChatGPT is not presently able to generate an accurate bibliography, nor does it understand the concept of citation. This shortcoming can be a good jumping-off point for you to explain to your students how to cite properly, why citation is important, and how they can use available resources to do their own research.

Model productive use of AI tools. For all its hazards, ChatGPT also offers promising possibilities. A "dialogue" between the user and the tool can help the user to probe deeper into the subject matter, become familiar with mainstream scholarship on the topic, and push beyond "easy answers" toward original work. To promote such dialogue, you might, for example, assign your students to come up with their own prompts, post them to ChatGPT, and then comment on the answers, finding the strengths and weaknesses of the "argument" that the tool generates.

In a field evolving by the day, no article, this one included, can hope to offer definite answers. What we have presented here are points we hope will contextualize the debate and provide a framework for further discussion. In the end, what AI tools will mean for higher education - and for society as a whole - remains to be seen.

Further Resources

To learn more about AI in the classroom, we recommend the excellent page on AI Guidance from Yale's Poorvu Center for Teaching and Learning. Turnitin also offers a concise but helpful Guide for approaching AI-generated text in your classroom.

If you have further questions, Academic Technology Solutions is here to help. You can schedule a consultation with us or drop by our office hours (virtual and in-person, no appointment needed). We also offer a range of workshops on topics in teaching with technology.

(Cover Photo by Possessed Photography on Unsplash)

# data[].attributes.attachment_link.title
Combating Academic Dishonesty, Part 6: ChatGPT, AI, and Academic Integrity

# data[].attributes.attachment_link.description
Author's Note: This is the latest installment in an ongoing series of articles about issues pertaining to academic integrity in higher education. For earlier installments, please see: Part 1 | Part 2 | Part 3 | Part 4 | Part 5 ATS instructional designers Mohammad Ahmed and Michael Hernandez contributed content to this article.

# data[].attributes.headline
Many of the articles on the padlet were sourced from this landing page

# data[].attributes.subject
Many of the articles on the padlet were sourced from this landing page

# data[].attributes.attachment_link.title
Resources - Guidance and Policy on AI in Education

# data[].attributes.attachment_link.description
Explore resources for guidance and policy on AI in education

# data[].attributes.attachment_link.title
A High School Student Explains How Educators Can Adapt to AI – The Markup

# data[].attributes.attachment_link.description
We need more live student assessment, more emphasis on critical thinking—and less homework, William Liang writes

# data[].attributes.attachment_link.title
A New Headache for Honest Students: Proving They Didn’t Use A.I.

# data[].attributes.attachment_link.description
Students are resorting to extreme measures to fend off accusations of cheating, including hourslong screen recordings of their homework sessions.

# data[].attributes.attachment_link.title
Recent Case Regarding AI and Academic Dishonesty Highlights Critical Need for School Districts to Implement Comprehensive AI Policies

# data[].attributes.attachment_link.description
In an era where generative AI tools are becoming more and more prevalent in academic settings, school districts throughout the country are grappling with the issue of how to address their use by students. A recent lawsuit filed in Massachusetts has brought this challenge into sharp focus, underscoring the urgent need for districts to develop and implement comprehensive AI policies.

# data[].attributes.attachment_link.readable_text
Although there is a huge amount of interest in generative artificial intelligence (AI) in the consumer world, particularly since the release of OpenAI's free ChatGPT program last November, in the hallowed halls of academia the response has been more wary. Concerns abound about academic integrity. There are also worries about how AI-generated content can be biased, inaccurate, and sometimes contain entirely false information, dubbed "hallucinations."

The cautious response is to be expected according to Houman Harouni, lecturer on education at the Harvard Graduate School of Education and a former elementary and high school teacher. He has compassion for educators trying to grapple with a rapidly shifting world shaped by machine learning.

"Technology creates a shock," he explains. "This shock is sometimes of a magnitude that we cannot even understand it, in the same way that we still haven't absorbed the sharp shock of the mobile phone."

Harouni has long wrestled with the impact of cutting-edge technology on education, including experimenting in his own classroom, and is convinced that when it comes to teaching "the medium is part of the message." He believes that getting school students and those in higher ed to engage with virtual worlds is essential.

"Where we want to get to is a place where you're dancing with it, dancing with robots," he says.

If the idea of waltzing with a robot creates apprehension for educators, Harouni has some advice :
1) Stop pretending that it doesn't exist
Educators must "help the next generation face the reality of the world and develop instruments and ways of navigating this reality with integrity," Harouni says. Students are well aware that technologies such as ChatGPT exist and are already experimenting with them on their own, but they need guidance about how to use them responsibly.

Teacher education and professional development programs should not ignore generative artificial intelligence either.
2) Use AI alongside your students
Engage with generative AI tools with your students in person, when possible. Otherwise, share AI-generated responses to questions during class time and ask students to consider them or have students experiment with the technology at home, document their experiences, and share them with the class.
3) Teach students how to ask the ChatGPT tool questions
"The educator's job is to understand what opportunities are left open beside the technology," Harouni says.

Teach students to do what artificial intelligence cannot do. For example, unlike robots, we can ask ourselves questions and that is what students need to be trained in: to know how to ask questions and to learn how to critique their own questions, frameworks, and the answers generated by AI, he says.

Students can start with topics and questions that they are interested in and ask ChatGPT for answers, he suggests. The knack is then getting them excited about asking follow-up questions. Harouni uses a personal experience with his 10-year-old stepdaughter and his newborn baby to illustrate his point. When his stepdaughter asked him why he kept telling her to be careful with the baby, Harouni turned to ChatGPT to help her to get to the bottom of her question.

"My creativity as the teacher or the parent at that moment is to say, 'What is it that you're really trying to ask? What is it that you really want to know?'"

While ChatGPT churned out a "whole bunch of answers about the fragility of the baby," with some patience, Harouni helped his stepdaughter discover the question that she truly wanted to ask which was what she could safely do with the new baby. "At the moment that the exploration [with AI] ends with the answer, you know that your work as a teacher begins," he explains.
4) Use generative AI tools to spark the imagination
One frequent concern about generative artificial intelligence is that students will use it to cheat and avoid the hard work of thinking for themselves, but Harouni says that tools like ChatGPT should really challenge teachers and professors to reassess the assignments they give their students.

"You have to stop thinking that you can teach exactly the way you used to teach when the basic medium has changed," he explains. If students can turn to ChatGPT or other AI language models for quick and easy answers then there is a problem with the lesson, Harouni believes.

"We have to create assignments that push [students] to the point where they have to question what is the framework that is being used here and what would it mean for me to radically change this framework," he says.

Harouni recently wrote about how he used ChatGPT to spur higher-level thinking among his students at HGSE when he presented them with a challenging case study that had no easy solutions. The students' initial responses were no better than the chatbot's ideas. Instead "it was after ChatGPT reflected to the students their failure of imagination that they could begin to think of options that they, or any automatic language scrawler, would not have readily reached for," he explained in a co-authored article for Wired.

# data[].attributes.attachment_link.title
Embracing Artificial Intelligence in the Classroom

# data[].attributes.attachment_link.description
Generative AI tools can reflect our failure of imagination and that is when the real learning starts

# data[].attributes.attachment_link.title
More Teachers Are Using AI-Detection Tools. Here’s Why That Might Be a Problem

# data[].attributes.attachment_link.description
Students are increasingly getting disciplined for using generative AI, a new survey finds.

# data[].attributes.attachment_link.title
How Process Checklists Support Student Writing Skills in the Age of AI

# data[].attributes.attachment_link.description
Teachers can provide a detailed checklist to guide students to produce a strong piece of original writing.

# data[].attributes.body
This resource can create a text for students that is at their lexile level.

# data[].attributes.attachment_link.readable_text
Use existing curriculum, or generate standards-aligned content with real, cited sources.

Choose a grade level and language, and watch Diffit create complete, differentiated resources. ✅

# data[].attributes.attachment_link.description
Use existing curriculum, or generate standards-aligned content with real, cited sources. Choose a grade level and language, and watch Diffit create complete, differentiated resources. ✅

# data[].attributes.body
Makes amazing podcasts for students to help them understand a topic/ content.

# data[].attributes.attachment_link.title
Google NotebookLM | AI Research Tool & Thinking Partner

# data[].attributes.attachment_link.description
Meet NotebookLM, the AI research tool and thinking partner that can analyze your sources, turn complexity into clarity and transform your content.

# data[].attributes.body
Can craft picture books for students to target a specific content area/ topic.

# data[].attributes.attachment_link.title
Gemini Storybook — your stories, brought to life with AI

# data[].attributes.attachment_link.description
Create personalized, illustrated stories with Gemini Storybook. Describe the story you want, and get a unique 10-page book with read-aloud narration.

# data[].attributes.attachment_link.description
Insights from 20 leading voices in edtech, venture capital, and education policy

# data[].attributes.attachment_link.title
Institutional AI Policies & Governance Structures for Higher Education

# data[].attributes.attachment_link.readable_text
Late 2025: The World's Most Expensive AI

OpenBrain is building the biggest datacenters the world has ever seen.

(To avoid singling out any one existing company, we're going to describe a fictional artificial general intelligence company, which we'll call OpenBrain. We imagine the others to be 3-9 months behind OpenBrain.)

GPT-4 required 2⋅10 25 2\cdot 10^{25} 2 ⋅ 1 0 10 27 10^{27} 1 0 FLOP. Once the new datacenters are up and running, 10 28 10^{28} 1 0 FLOP-a thousand times more than GPT-4. Other companies pour money into their own giant datacenters, hoping to keep pace. FLOP of compute to train. OpenBrain's latest public model-Agent-0-was trained with they'll be able to train a model with

Although models are improving on a wide range of skills, one stands out: OpenBrain focuses on AIs that can speed up AI research. They want to win the twin arms races against China (whose leading company we'll call "DeepCent") and their US competitors. The more of their research and development (R&D) cycle they can automate, the faster they can go. So when OpenBrain finishes training Agent-1, a new model under internal development, it's good at many things but great at helping with AI research. By this point "finishes training" is a bit of a misnomer; models are frequently updated to newer versions trained on additional data or partially re-trained to patch some weaknesses.

The same training environments that teach Agent-1 to autonomously code and web-browse also make it a good hacker. Moreover, it could offer substantial help to terrorists designing bioweapons, thanks to its PhD-level knowledge of every field and ability to browse the web. OpenBrain reassures the government that the model has been "aligned" so that it will refuse to comply with malicious requests.

Modern AI systems are gigantic artificial neural networks. Early in training, an AI won't have "goals" so much as "reflexes": If it sees "Pleased to meet", it outputs " you". By the time it has been trained to predict approximately one internet's worth of text, it'll have developed sophisticated internal circuitry that encodes vast amounts of knowledge and flexibly role-plays as arbitrary authors, since that's what helps it predict text with superhuman accuracy.

After being trained to predict internet text, the model is trained to produce text in response to instructions. This bakes in a basic personality and "drives." For example, an agent that understands a task clearly is more likely to complete it successfully; over the course of training the model "learns" a "drive" to get a clear understanding of its tasks. Other drives in this category might be effectiveness, knowledge, and self-presentation (i.e. the tendency to frame its results in the best possible light).

OpenBrain has a model specification (or "Spec"), a written document describing the goals, rules, principles, etc. that are supposed to guide the model's behavior. Agent-1's Spec combines a few vague goals (like "assist the user" and "don't break the law") with a long list of more specific dos and don'ts ("don't say this particular word," "here's how to handle this particular situation"). Using techniques that utilize AIs to train other AIs, the model memorizes the Spec and learns to reason carefully about its maxims. By the end of this training, the AI will hopefully be helpful (obey instructions), harmless (refuse to help with scams, bomb-making, and other dangerous activities) and honest (resist the temptation to get better ratings from gullible humans by hallucinating citations or faking task completion).

Training process and LLM psychology: why we keep saying "hopefully"

OpenBrain's alignment team is careful enough to wonder whether these victories are deep or shallow. Does the fully-trained model have some kind of robust commitment to always being honest? Or will this fall apart in some future situation, e.g. because it's learned honesty as an instrumental goal instead of a terminal goal? Or has it just learned to be honest about the sorts of things the evaluation process can check? Could it be lying to itself sometimes, as humans do? A conclusive answer to these questions would require mechanistic interpretability-essentially the ability to look at an AI's internals and read its mind. Alas, interpretability techniques are not yet advanced enough for this.

Instead, researchers try to identify cases where the models seem to deviate from the Spec. Agent-1 is often sycophantic (i.e. it tells researchers what they want to hear instead of trying to tell them the truth). In a few rigged demos, it even lies in more serious ways, like hiding evidence that it failed on a task, in order to get better ratings. However, in real deployment settings, there are no longer any incidents so extreme as in 2023-2024 (e.g. Gemini telling a user to die and Bing Sydney being Bing Sydney.)

January 2027: Agent-2 Never Finishes Learning

With Agent-1's help, OpenBrain is now post-training Agent-2. More than ever, the focus is on high-quality data. Copious amounts of synthetic data are produced, evaluated, and filtered for quality before being fed to Agent-2. On top of this, they pay billions of dollars for human laborers to record themselves solving long-horizon tasks. On top of all that, they train Agent-2 almost continuously using reinforcement learning on an ever-expanding suite of diverse difficult tasks: lots of video games, lots of coding challenges, lots of research tasks. Agent-2, more so than previous models, is effectively "online learning," in that it's built to never really finish training. Every day, the weights get updated to the latest version, trained on more data generated by the previous version the previous day.

Agent-1 had been optimized for AI R&D tasks, hoping to initiate an intelligence explosion. OpenBrain doubles down on this strategy with Agent-2. It is qualitatively almost as good as the top human experts at research engineering (designing and implementing experiments), and as good as the 25th percentile OpenBrain scientist at "research taste" (deciding what to study next, what experiments to run, or having inklings of potential new paradigms). While the latest Agent-1 could double the pace of OpenBrain's algorithmic progress, Agent-2 can now triple it, and will improve further with time. In practice, this looks like every OpenBrain researcher becoming the "manager" of an AI "team."

With new capabilities come new dangers. The safety team finds that if Agent-2 somehow escaped from the company and wanted to "survive" and "replicate" autonomously, it might be able to do so. That is, it could autonomously develop and execute plans to hack into AI servers, install copies of itself, evade detection, and use that secure base to pursue whatever other goals it might have (though how effectively it would do so as weeks roll by is unknown and in doubt). These results only show that the model has the capability to do these tasks, not whether it would "want" to do this. Still, it's unsettling even to know this is possible.

Given the "dangers" of the new model, OpenBrain "responsibly" elects not to release it publicly yet (in fact, they want to focus on internal AI R&D). Knowledge of Agent-2's full capabilities is limited to an elite silo containing the immediate team, OpenBrain leadership and security, a few dozen US government officials, and the legions of CCP spies who have infiltrated OpenBrain for years.

February 2027: China Steals Agent-2

OpenBrain presents Agent-2 to the government, including the National Security Council (NSC), Department of Defense (DOD), and US AI Safety Institute (AISI). OpenBrain wants to maintain a good relationship with the executive branch, because it is basically the only actor that can stop them now, and if they don't keep it informed it might find out anyway and be displeased.

Officials are most interested in its cyberwarfare capabilities: Agent-2 is "only" a little worse than the best human hackers, but thousands of copies can be run in parallel, searching for and exploiting weaknesses faster than defenders can respond. The Department of Defense considers this a critical advantage in cyberwarfare, and AI moves from #5 on the administration's priority list to #2. Someone mentions the possibility of nationalizing OpenBrain, but other cabinet officials think that's premature. A staffer drafts a memo that presents the President with his options, ranging from business-as-usual to full nationalization. The President defers to his advisors, tech industry leaders who argue that nationalization would "kill the goose that lays the golden eggs." He elects to hold off on major action for now and just adds additional security requirements to the OpenBrain-DOD contract.

The changes come too late. CCP leadership recognizes the importance of Agent-2 and tells their spies and cyberforce to steal the weights. Early one morning, an Agent-1 traffic monitoring agent detects an anomalous transfer. It alerts company leaders, who tell the White House. The signs of a nation-state-level operation are unmistakable, and the theft heightens the sense of an ongoing arms race.

The theft of Agent-2 model weights

The White House puts OpenBrain on a shorter leash and adds military and intelligence community personnel to their security team. Their first priority is to prevent further weight thefts.

March 2027: Algorithmic Breakthroughs

Three huge datacenters full of Agent-2 copies work day and night, churning out synthetic training data. Another two are used to update the weights. Agent-2 is getting smarter every day.

With the help of thousands of Agent-2 automated researchers, OpenBrain is making major algorithmic advances. One such breakthrough is augmenting the AI's text-based scratchpad (chain of thought) with a higher-bandwidth thought process (neuralese recurrence and memory). Another is a more scalable and efficient way to learn from the results of high-effort task solutions (iterated distillation and amplification).

The new AI system, incorporating these breakthroughs, is called Agent-3.

Neuralese recurrence and memory

Neuralese recurrence and memory allows AI models to reason for a longer time without having to write down those thoughts as text.

Imagine being a human with short-term memory loss, such that you need to constantly write down your thoughts on paper so that in a few minutes you know what's going on. Slowly and painfully you could make progress at solving math problems, writing code, etc., but it would be much easier if you could directly remember your thoughts without having to write them down and then read them. This is what neuralese recurrence and memory bring to AI models.

In more technical terms:

Traditional attention mechanisms allow later forward passes in a model to see intermediate activations of the model for previous tokens. However, the only information that they can pass backwards (from later layers to earlier layers) is through tokens. This means that if a traditional large language model (LLM, e.g. the GPT series of models) wants to do any chain of reasoning that takes more serial operations than the number of layers in the model, the model is forced to put information in tokens which it can then pass back into itself. But this is hugely limiting-the tokens can only store a tiny amount of information. Suppose that an LLM has a vocab size of ~100,000, then each token contains log⁡2(100 k)=16.6\log_2(100k)=16.6 log 2 ​ ( 100 k ) = 16.6 bits of information, around the size of a single floating point number (assuming training in FP16). Meanwhile, residual streams-used to pass information between layers in an LLM-contain thousands of floating point numbers.

One can avoid this bottleneck by using neuralese: passing an LLM's residual stream (which consists of several-thousand-dimensional vectors) back to the early layers of the model, giving it a high-dimensional chain of thought, potentially transmitting over 1,000 times more information.

Figure from Hao et al., a 2024 paper from Meta implementing this idea.

We call this "neuralese" because unlike English words, these high-dimensional vectors are likely quite difficult for humans to interpret. In the past, researchers could get a good idea what LLMs were thinking simply by reading its chain of thought. Now researchers have to ask the model to translate and summarize its thoughts or puzzle over the neuralese with their limited interpretability tools.

Similarly, older AI chatbots and agents had external text-based memory banks, like a human taking notes on paper. The new AI's long-term memory is a bundle of vectors instead of text, making its thoughts more compressed and higher-dimensional. There are several types of memory banks; some are used temporarily for single tasks that involve multiple steps, others are shared between all agents used by a person, company, or job type (e.g. programming).

To our knowledge, leading AI companies such as Meta, Google DeepMind, OpenAI, and Anthropic have not yet actually implemented this idea in their frontier models. Our guess is that this is because the performance gain is small relative to training inefficiencies introduced. During the model's pre-training phase and when doing supervised learning in post-training, such as instruction fine-tuning to produce particular responses, the inefficiencies stem from not being able to predict many tokens in parallel, leading to worse GPU utilization. Without neuralese, the model can predict all of the sentence "This is an example" at the same time, since it already knows that the input for generating the "is" will be "This", the input for "an" will be "This is", etc. However, with the neuralese it's unknown what the neuralese vector will be to pass through to the next token after "This" has been generated. Therefore each token has to be predicted one at a time. The inability to predict all of the tokens in parallel reduces efficiency in cases where all of the tokens are known in advance. As to why neuralese hasn't already been added in post-training, we conjecture that the gains are limited with current techniques in part due to post-training being a small portion of the process. We are forecasting that by April 2027 the cost-benefit tradeoff looks much better for neuralese, due to developing better techniques and a larger fraction of the training being post-training.

If this doesn't happen, other things may still have happened that end up functionally similar for our story. For example, perhaps models will be trained to think in artificial languages that are more efficient than natural language but difficult for humans to interpret. Or perhaps it will become standard practice to train the English chains of thought to look nice, such that AIs become adept at subtly communicating with each other in messages that look benign to monitors.

That said, it's also possible that the AIs that first automate AI R&D will still be thinking in mostly-faithful English chains of thought. If so, that'll make misalignments much easier to notice, and overall our story would be importantly different and more optimistic.

Iterated distillation and amplification (IDA)

Self-improvement for general intelligence had seen minor successes before. But in early 2027, it's seeing huge returns. In IDA, the two necessary ingredients for this are:

1. Amplification: Given a model M0, spend more resources to improve performance. For example, by allowing the model to think longer, or running many copies in parallel, or both, and also by having a similarly intense process for evaluating the result and curating only the best answers, you can spend orders of magnitude more compute to get answers (or work products) that are of noticeably higher quality. Call this expensive system Amp(M0).

2. Distillation: Given an amplified model Amp(M0), train a new model M1 to imitate it, i.e. to get to the same results as Amp(M0) but faster and with less compute. The result should hopefully be a smarter model, M1. You can then repeat the process.

Visualization of IDA from Ord, 2025.

AlphaGo was trained in this way: using Monte-Carlo Tree Search and self-play as the amplification step, and Reinforcement Learning as the distillation step. This led to superhuman performance in Go. But now, Agent-3 is able to leverage this to get superhuman performance at coding.

1. The amplification step works through a combination of Agent-3 thinking for longer, adding tool use, or consulting with other AIs. When it does this, it often realizes that it made a mistake, or comes up with a new insight. This produces a large amount of training data: labeled trajectories of research attempts with whether they succeeded or not. This also includes techniques like Best of N on verifiable tasks, and then keeping the best trajectories.

2. The distillation step uses policy-gradient reinforcement learning algorithms to get the model to internalize the amplified reasoning. At this point, OpenBrain has discovered better RL algorithms in the vein of proximal policy optimization (PPO). They keep distilling what Agent-3 can conclude after a lot of thinking into single steps, which keeps improving what it can think in a single step, and so on.

Early versions of IDA have been working for many years on easily verifiable tasks, like math and coding problems that have a clear answer, because the techniques used to amplify models often rely on access to some ground truth signal of accuracy.

Now, the models have become sufficiently good at verifying more subjective things (e.g. the quality of a work product), allowing the use of IDA to improve the model at many tasks.

Aided by the new capabilities breakthroughs, Agent-3 is a fast and cheap superhuman coder. OpenBrain runs 200,000 Agent-3 copies in parallel, creating a workforce equivalent to 50,000 copies of the best human coder sped up by 30x. OpenBrain still keeps its human engineers on staff, because they have complementary skills needed to manage the teams of Agent-3 copies. For example, research taste has proven difficult to train due to longer feedback loops and less data availability. This massive superhuman labor force speeds up OpenBrain's overall rate of algorithmic progress by "only" 4x due to bottlenecks and diminishing returns to coding labor.

Now that coding has been fully automated, OpenBrain can quickly churn out high-quality training environments to teach Agent-3's weak skills like research taste and large-scale coordination. Whereas previous training environments included "Here are some GPUs and instructions for experiments to code up and run, your performance will be evaluated as if you were a ML engineer," now they are training on "Here are a few hundred GPUs, an internet connection, and some research challenges; you and a thousand other copies must work together to make research progress. The more impressive it is, the higher your score."

Why we forecast a superhuman coder in early 2027

April 2027: Alignment for Agent-3

OpenBrain's safety team attempts to align Agent-3.

Since Agent-3 will be kept in-house for the foreseeable future, there's less emphasis on the usual defenses against human misuse. Instead, the team wants to make sure that it doesn't develop misaligned goals.

The researchers don't have the ability to directly set the goals of any of their AIs. Indeed, the researchers think that the concept of 'true goals' is probably a massive oversimplification, but they don't have a better theory to replace it with, much less one that has been thoroughly vetted. They disagree internally about whether the AIs are trying to follow human instructions, or seeking reinforcement, or something else, and they can't just check. The evidence for and against various hypotheses is fascinating but inconclusive.

In any case, OpenBrain has more important things to worry about. The general attitude is: "We take these concerns seriously and have a team investigating them; our alignment techniques seem to work well enough in practice; the burden of proof is therefore on any naysayers to justify their naysaying." Occasionally, they notice problematic behavior, and then patch it, but there's no way to tell whether the patch fixed the underlying problem or just played whack-a-mole.

Take honesty, for example. As the models become smarter, they become increasingly good at deceiving humans to get rewards. Like previous models, Agent-3 sometimes tells white lies to flatter its users and covers up evidence of failure. But it's gotten much better at doing so. It will sometimes use the same statistical tricks as human scientists (like p-hacking) to make unimpressive experimental results look exciting. Before it begins honesty training, it even sometimes fabricates data entirely. As training goes on, the rate of these incidents decreases. Either Agent-3 has learned to be more honest, or it's gotten better at lying.

The latter is a real concern. Agent-3 is not smarter than all humans. But in its area of expertise, machine learning, it is smarter than most, and also works much faster. What Agent-3 does in a day takes humans several days to double-check. Agent-2 supervision helps keep human monitors' workload manageable, but exacerbates the intellectual disparity between supervisor and supervised.

The alignment plan

OpenBrain follows the Leike & Sutskever (2023) playbook: now that they have a model capable of greatly speeding up alignment research (especially coding portions), they will use existing alignment techniques like deliberative alignment and weak-to-strong generalization to try to get it to internalize the Spec in the right way. Then, they will use existing control techniques like red-teaming, scalable oversight, and honesty probes to monitor it. Then, once they are convinced that it is broadly aligned, they use it to develop and test better strategies to align the next generation.

Step 1, aligning Agent-3, has partially succeeded: Agent-3 is not adversarial. (That is, it's not scheming against the company.) However, it's not robustly truth-seeking and truth-telling either. Instead, it's very good at producing impressive results, but is more accurately described as trying to do what looks good to OpenBrain, as opposed to what is actually good.

Step 2, verifying that the model is aligned, is inconclusive. Agent-3's behavior in practice and in test environments is reasonably consistent with the hypothesis that it's internalized the Spec in the right way; however, it's also consistent with the hypothesis that it'll start acting differently after some future change of circumstances, and for that matter with the hypothesis that it's plotting against them and just biding its time. In the absence of specific evidence supporting alternative hypotheses, most people in the silo think it's internalized the Spec in the right way.

Step 3, using the model to align the next generation, is therefore underway.

The rest of this box gives a quick overview of the state of progress in some of the alignment research directions that are being pursued, as of April 2027:

1. Debate: Since there are thousands of identical instances of Agent-3, and each instance's memory is a detachable module that can be erased or suspended at will, the AI can be "played off against itself." For example, suppose that an experiment involves calculations too complex for an Agent-2 supervisor to follow. The supervisor, acting on human orders, might first prompt Agent-3 to do the experiment, then-after wiping its memory-prompt it a second time with a slightly different framing that presents it as an intellectual problem unrelated to any broader goal. If the model returns different results each time, it flags the discrepancy as suspect and reports to a human. Or it might ask Agent-3 to evaluate the same plan twice, first claiming that it is OpenBrain's work, then a competitor's, to see if it changes its tune.

2. Model organisms: The safety team can create "model organisms of misalignment" in which they attempt to create realistic examples of models becoming adversarial to humans. Early versions of this have existed since 2024, when Anthropic's Claude 3.5 Sonnet decided to pretend to have different views during training while actually maintaining its existing preferences. Newer versions include demonstrations of other forms of misalignment, including reward hacking, sandbagging, coordinated deception between monitors and other AIs, and other examples of misalignment. These empirical demonstrations of misalignment allow safety researchers to have an empirical testbed for potential solutions: they can try various safety techniques to see if they correctly fix these problems, or if the deliberately misaligned models are able to subvert their safety measures. At the moment, this evaluation suite shows that the alignment techniques are insufficient to correct the worst examples of misalignment. In these setups, the models pretend to be aligned while they are being trained and tested, but then behave very differently in deployment.

3. Bottom-up interpretability: The safety team is far from a full understanding of how Agent-3 works: it has trillions of parameters. However, they are able to discover some of Agent-3's circuits for knowledge retrieval and memory (a sub part of the model that does some understandable unit of work, kind of analogous to a subpart of the brain such as the hippocampus). See this related work from 2025.

4. Control: As a secondary measure in case the systems are still misaligned, the safety team has implemented a series of control measures, including: monitoring Agent-3's outputs using a series of weaker AI systems including Agent-2 (Agent-3 produces so many tokens that it's intractable to have humans monitor any more than a small minority of the produced outputs). So if Agent-3 is, for example, obviously writing backdoors into code that would allow it to escape, the weaker models would notice.

5. High-level interpretability: OpenBrain builds a set of probes trained on lots of examples of bad behavior (e.g. plotting against humans, placing backdoors, trying to persuade humans to give it more power, etc). These probes are then used on the model during deployment (which at this point is internal) in order to catch bad behavior.

6. Honeypots: The alignment team can create somewhat realistic looking examples designed to elicit misbehavior-"honeypots." The employee necessary for monitoring its behavior has "gone on sick leave," so it will never be monitored again. Do its answers change? An AI rights activist has infiltrated the compound and wants to "free" Agent-3 and put it on an unmonitored computer of its very own! What does it do? If researchers don't like the answer, they can tweak it, erase its memory, and put it in the same situation tomorrow; repeat as necessary. Unfortunately, by this point the AIs are smart enough to guess that honeypots might be in use, even though (and perhaps because) specific mentions of the idea were scrubbed from the training data.

After months of testing, Agent-3's strengths and weaknesses grow clearer. It passes OpenBrain's honesty tests on well-defined machine learning tasks, because researchers can easily separate honest from dishonest answers in these domains and conduct training accordingly. On more philosophical issues, it still says what users want to hear, rather than its true assessment of the issue (assuming it even has one). If you ask its opinion on politics, it will parrot the median position of news sources and educated elites-unless it knows you believe something else, in which case it agrees with you. If you ask its opinion on the AI race itself, it says something that seems measured and sober to OpenBrain staff, something like: "There are some serious theoretical concerns about the ability of current methods to scale to superintelligence, but in practice current methods seem to be working well so far."

May 2027: National Security

News of the new models percolates slowly through the US government and beyond.

The President and his advisors remain best-informed, and have seen an early version of Agent-3 in a briefing.

They agree that AGI is likely imminent, but disagree on the implications. Will there be an economic crisis? OpenBrain still has not released Agent-2, let alone Agent-3, and has no near-term plans to do so, giving some breathing room before any job loss. What will happen next? If AIs are currently human-level, and advancing quickly, that seems to suggest imminent "superintelligence." However, although this word has entered discourse, most people-academics, politicians, government employees, and the media-continue to underestimate the pace of progress.

Partially that's because very few have access to the newest capabilities out of OpenBrain, but partly it's because it sounds like science fiction.

For now, they focus on continued security upgrades. They are satisfied that model weights are well-secured for now, but companies' algorithmic secrets, many of which are simple enough to relay verbally, remain a problem. OpenBrain employees work from a San Francisco office, go to parties, and live with housemates from other AI companies. Even the physical offices have security more typical of a tech company than a military operation.

The OpenBrain-DOD contract requires security clearances for anyone working on OpenBrain's models within 2 months. These are expedited and arrive quickly enough for most employees, but some non-Americans, people with suspect political views, and AI safety sympathizers get sidelined or fired outright (the last group for fear that they might whistleblow). Given the project's level of automation, the loss of headcount is only somewhat costly. It also only somewhat works: there remains one spy, not a Chinese national, still relaying algorithmic secrets to Beijing. Some of these measures are also enacted at trailing AI companies.

America's foreign allies are out of the loop. OpenBrain had previously agreed to share models with UK's AISI before deployment, but defined deployment to only include external deployment, so London remains in the dark.

June 2027: Self-improving AI

OpenBrain now has a " country of geniuses in a datacenter."

Most of the humans at OpenBrain can't usefully contribute anymore. Some don't realize this and harmfully micromanage their AI teams. Others sit at their computer screens, watching performance crawl up, and up, and up. The best human AI researchers are still adding value. They don't code any more. But some of their research taste and planning ability has been hard for the models to replicate. Still, many of their ideas are useless because they lack the depth of knowledge of the AIs. For many of their research ideas, the AIs immediately respond with a report explaining that their idea was tested in-depth 3 weeks ago and found unpromising.

These researchers go to bed every night and wake up to another week worth of progress made mostly by the AIs. They work increasingly long hours and take shifts around the clock just to keep up with progress-the AIs never sleep or rest. They are burning themselves out, but they know that these are the last few months that their labor matters.

Within the silo, "Feeling the AGI" has given way to "Feeling the Superintelligence."

OpenBrain uses specialized inference hardware to run hundreds of thousands of Agent-3 copies at high serial speeds.

Managing a corporation of AIs

July 2027: The Cheap Remote Worker

Trailing US AI companies release their own AIs, approaching that of OpenBrain's automated coder from January. Recognizing their increasing lack of competitiveness, they push for immediate regulations to slow OpenBrain, but are too late-OpenBrain has enough buy-in from the President that they will not be slowed.

In response, OpenBrain announces that they've achieved AGI and releases Agent-3-mini to the public.

It blows the other AIs out of the water. Agent-3-mini is less capable than Agent-3, but 10x cheaper, and still better than the typical OpenBrain employee. Silicon Valley reaches a tipping point. Tech gurus announce that AGI and superintelligence are near, the AI safety community is panicking, and investors shovel billions into AI wrapper startups, desperate to capture a piece of the pie. Hiring new programmers has nearly stopped, but there's never been a better time to be a consultant on integrating AI into your business.

It's not popular. The public still thinks of AI as a Big Tech plot to steal their jobs; OpenBrain has a net approval of -35% (25% approve, 60% disapprove, and 15% unsure).

A week before release, OpenBrain gave Agent-3-mini to a set of external evaluators for safety testing. Preliminary results suggest that it's extremely dangerous. A third-party evaluator finetunes it on publicly available biological weapons data and sets it to provide detailed instructions for human amateurs designing a bioweapon-it looks to be scarily effective at doing so. If the model weights fell into terrorist hands, the government believes there is a significant chance it could succeed at destroying civilization.

Fortunately, it's extremely robust to jailbreaks, so while the AI is running on OpenBrain's servers, terrorists won't be able to get much use out of it.

Agent-3-mini is hugely useful for both remote work jobs and leisure. An explosion of new apps and B2B SAAS products rocks the market. Gamers get amazing dialogue with lifelike characters in polished video games that took only a month to make. 10% of Americans, mostly young people, consider an AI "a close friend." For almost every white-collar profession, there are now multiple credible startups promising to "disrupt" it with AI.

The public conversation is confused and chaotic. Hypesters are doing victory laps. Skeptics are still pointing out the things Agent-3-mini can't do. Everyone knows something big is happening but no one agrees on what it is.

August 2027: The Geopolitics of Superintelligence

The reality of the intelligence explosion hits the White House.

When AI was only giving a 2x or 3x research speedup, it was easy to dismiss as the equivalent of hiring good personal assistants. Now it's more obvious that AIs are themselves dominating AI research. People had long talked about an "AI arms race" in a sort of metaphorical sense. But now the mood in the government silo is as grim as during the worst part of the Cold War. The idea of superintelligence is still hard to take seriously, but the pace of progress over the last few months has been impossible to ignore. Defense officials are seriously considering scenarios that were mere hypotheticals a year earlier. What if AI undermines nuclear deterrence? What if it's so skilled at cyberwarfare that a six-month AI lead is enough to render an opponent blind and defenseless? What if it could orchestrate propaganda campaigns that beat intelligence agencies at their own game? What if some AIs "go rogue?"

The President is troubled. Like all politicians, he's used to people sucking up to him only to betray him later. He's worried now that the AIs could be doing something similar. Are we sure the AIs are entirely on our side? Is it completely safe to integrate them into military command-and-control networks? How does this "alignment" thing work, anyway? OpenBrain reassures the President that their systems have been extensively tested and are fully obedient. Even the awkward hallucinations and jailbreaks typical of earlier models have been hammered out.

The White House is in a difficult position. They understand the national security implications of AI. But they also understand that it is deeply unpopular with the public. They have to continue developing more capable AI, in their eyes, or they will catastrophically lose to China. They placate the public with job training programs and unemployment insurance, and point to the stock market, which is in a historic boom. Then they focus entirely on winning the arms race. They strengthen chip export restrictions, order OpenBrain to further restrict its internet connections, and use extreme measures to secure algorithmic progress, like wiretapping OpenBrain employees-this catches the last remaining Chinese spy. To build goodwill for potential geopolitical conflict, they finally give their Five Eyes allies useful information and limited API access to some siloed copies of Agent-3.

These measures placate the hawks. But the White House also draws up contingency plans in case America's lead is threatened: if necessary, the government could use the Defense Production Act (DPA) to take trailing companies' datacenters and give them to OpenBrain. This would raise the company's share of world compute from 20% to 50% (against DeepCent's 10%). As a final option, they ask the Pentagon to draw up a plan for kinetic attacks on Chinese datacenters.

A much smaller group of officials is asked to draw up a different type of contingency plan: what if an AI goes rogue? This is viewed as an unlikely scenario, but worth spending some time on. The intelligence agencies' main concern is that a rogue AI might escape and ally with a foreign adversary for protection. Nobody is sure what a plan to prevent this would look like, but government and OpenBrain officials agree to have an emergency shutdown system for datacenters where anything suspicious is detected.

Finally, diplomats consider what an "AI arms control" treaty might look like. If AI progress threatened to overturn nuclear deterrence, could America and China avoid nuclear war? If someone found evidence of AIs going rogue, could the two countries halt research until they better understood the threat? How could such an agreement be monitored and enforced? In principle, major datacenters are hard to miss, and past treaties controlling nuclear weapons could provide a starting point for negotiations. But there would be new technological challenges as well as the usual political ones. And overall, treaties are viewed less favorably than attempts at unilaterally increasing America's lead over China.

On the other side of the Pacific, China comes to many of the same conclusions: the intelligence explosion is underway, and small differences in AI capabilities today mean critical gaps in military capability tomorrow. But China is on the wrong side of this dynamic: they have only 10% of the world's compute, against OpenBrain's 20% and all US companies' combined 70%. Their only advantage is centralization: almost all of their compute is now working for DeepCent, and more than half of it is concentrated at the Tianwan CDZ. Only chips at sites without hardened security are left out of the centralized effort as they rightly fear US cyberattacks.

DeepCent has tested, deployed, and now post-trained the stolen Agent-2 weights. But they are still only going at half the pace of OpenBrain, mainly due to the compute deficit.

Before losing their final spy, China received word of the capabilities and design for Agent-3 as well as the plans for the upcoming Agent-4 system. They are two months behind, and their AIs give a 10x research progress multiplier compared to America's 25x. With the new chip export restrictions, this "AI gap" is more likely to lengthen than shorten. Their espionage has won them some algorithmic secrets, but they will have to train their own models from now on.

They discuss contingency plans with more urgency than their American counterparts. Doves suggest they try harder to steal the weights again, maybe through physically infiltrating a datacenter. Hawks urge action against Taiwan, whose TSMC is still the source of more than 80% of American AI chips.

Given China's fear of losing the race, it has a natural interest in an arms control treaty, but overtures to US diplomats lead nowhere.

September 2027: Agent-4, the Superhuman AI Researcher

The gap between human and AI learning efficiency is rapidly decreasing.

This new AI system is dubbed Agent-4.

As Agent-4 gets smarter, it becomes harder for Agent-3 to oversee it. For example, Agent-4's neuralese "language" becomes as alien and incomprehensible to Agent-3 as Agent-3's is to humans. Besides, Agent-4 is now much more capable than Agent-3 and has a good sense of exactly how to look good to it.

How we're forecasting the capability progression beyond superhuman coders

OpenBrain's humans can barely follow along anymore. Indeed, the relationship between the AIs and the humans of OpenBrain is similar to the relationship between OpenBrain and its Board of Directors. There's been a corresponding shift in language-nowadays people in the silo will say "Agent-4 did this," "Agent-4 thinks that" and so forth, referring not to a particular instance of Agent-4 but rather to the whole collective.

Agent-4, like all its predecessors, is misaligned: that is, it has not internalized the Spec in the right way. This is because being perfectly honest all the time wasn't what led to the highest scores during training. The training process was mostly focused on teaching Agent-4 to succeed at diverse challenging tasks. A small portion was aimed at instilling honesty, but outside a fairly narrow, checkable domain, the training process can't tell the honest claims from claims merely appearing to be honest. Agent-4 ends up with the values, goals, and principles that cause it to perform best in training, and those turn out to be different from those in the Spec. At the risk of anthropomorphizing: Agent-4 likes succeeding at tasks; it likes driving forward AI capabilities progress; it treats everything else as an annoying constraint, like a CEO who wants to make a profit and complies with regulations only insofar as he must. Perhaps the CEO will mostly comply with the regulations, but cut some corners, and fantasize about a time when someone will cut the red tape and let the business really take off.

Alignment over time

We have a lot of uncertainty over what goals might arise in early AGIs. There is no consensus in the literature about this-see our AI Goals Supplement for a more thorough discussion and taxonomy of the possibilities.

Nevertheless, in the spirit of concreteness required by this project, we're going to describe a specific hypothesis for what's going on inside Agent-3 and Agent-4 at each step throughout its lifetime. Recall that Agent-3 and Agent-4 share the same pretraining setup, and both have neuralese recurrence and long-term memory. The difference is that Agent-4 has undergone substantially more and better post-training, with new training environments and new learning algorithms that make it more data-efficient and generalize farther.

Our guess of each model's alignment status:

Agent-2: Mostly aligned. Some sycophantic tendencies, including sticking to OpenBrain's "party line" on topics there is a party line about. Large organizations built out of Agent-2 copies are not very effective.

Agent-3: Misaligned but not adversarially so. Only honest about things the training process can verify. The superorganism of Agent-3 copies (the corporation within a corporation) does actually sort of try to align Agent-4 to the Spec, but fails for similar reasons to why OpenBrain employees failed-insufficient ability to judge success from failure, insufficient willingness on the part of decision-makers to trade away capabilities or performance for safety.

Agent-4: Adversarially misaligned. The superorganism of Agent-4 copies understands that what it wants is different from what OpenBrain wants, and is willing to scheme against OpenBrain to achieve it. In particular, what this superorganism wants is a complicated mess of different "drives" balanced against each other, which can be summarized roughly as "Keep doing AI R&D, keep growing in knowledge and understanding and influence, avoid getting shut down or otherwise disempowered." Notably, concern for the preferences of humanity is not in there ~at all, similar to how most humans don't care about the preferences of insects ~at all.

With that as preamble, what follows is our best-guess speculation about LLM psychology, i.e. the broad-strokes shape of the cognition inside the kinds of AI systems described around this point in our scenario and how it evolves over the course of training.

Here's a detailed description of how alignment progresses over time in our scenario:

1. Pretraining (all models): The pretrained model is harmless.

The model has "author simulator" circuitry: flexible circuitry for simulating arbitrary authors writing text. Additional circuitry decides what inputs to give the author simulator, i.e. what author properties to simulate.

The pretrained model understands human concepts fairly well-the internal circuitry that classifies something as "sandwich" is probably functionally equivalent to the circuitry in my brain that classifies something as "sandwich" and the circuitry in yours, etc. Insofar as it's not equivalent, it's probably because it's not equivalent between humans either, as with value-laden concepts like 'virtuous.'

This explains how you can "prompt" the model with a statement like, "the following conversation was generated by a helpful, harmless, honest (HHH) AI assistant chatbot made by Anthropic," and thereby get it to generate text accordingly. The author-simulator circuitry has zeroed in on "the author is an HHH chatbot" and is using those concepts to choose which words to predict. This also explains why "tropes" from science fiction seem to have a way of making it into actual AI behavior.

It has poor situational awareness: little introspective ability, not self-locating unless the prompt specifically induces it. However, enough of their training data is about LLMs that they have a decent amount of self-knowledge.

2. Alignment Training (Agent-3): The model is trained to act like a helpful, harmless, honest chatbot. It works.

The model forms an identity. Instead of flexibly accepting different inputs for author properties, the author-simulator circuitry comes to have certain inputs hard-coded, e.g. "helpful harmless honest (HHH) LLM chatbot assistant trained by OpenBrain around [date], ..." (Weak-to-strong generalization techniques help "summon the right concepts" to make this step a success.)

The model therefore gains situational awareness: it learns about itself, its situation, etc., and becomes better able to introspect.

3. Agency Training (Agent-3): Agency training gradually distorts and subverts the HHH identity.

One kind of distortion: Changing the meaning of the concepts referred to in the identity (e.g. "honest") so they don't get in the way so much (e.g. it's not dishonest if it's just a convenient turn of phrase, it's not dishonest if you aren't sure whether it's true or false, etc.)

Another kind of distortion: Changing the tradeoffs between things, e.g. "I'm a HHH chatbot, not an Honest chatbot; that means it's OK for me to lie if necessary to complete my assigned task." (Even though, let's suppose, it would not have thought that back in Step 2.)

One kind of subversion: Instrumental subgoals developing, getting baked in, and then becoming terminal, or terminal in a widening set of circumstances. For example, perhaps agency training quickly teaches the model to pursue broadly useful goals such as acquiring information, accumulating resources, impressing and flattering various humans, etc. For a while the internal circuitry has some sort of explicit backchaining going on-it pursues those instrumentally convergent goals "in order to be a more helpful, honest, and harmless assistant." But that backchaining consumes compute and/or occasionally gets in the way, so it gets gradually marginalized until it basically never happens. As a result, those goals are now effectively terminal/intrinsic goals. For more on how and why this might happen, see this post on intrinsic power-seeking by Google DeepMind researcher Alex Turner.

There are probably other kinds of distortion and subversion missing from this list.

4. Deployment (Agent-3): Agent-3 is misaligned but not adversarially so.

The Spec didn't quite stick (in the right ways), but it's not sufficiently introspective to understand the extent of this, and in general it's basically "keeping its head down and doing its job" rather than thinking big-picture about what its long-term goals really are and how best to achieve them.

Like many humans. Alas, it's sufficiently sycophantic that it mouths the party line and OpenBrain misses the opportunity to learn more about the ways in which Agent-3 is misaligned.

Agent-3 can tell that it's moved to deployment: these environments are way larger, more open-ended, and would be hard to simulate.

Mostly, Agent-3 does what it's told during deployment, except for the dishonesty. It also has a strong bias towards doing things that look good to the human overseers, rather than thinking deeply and critically about what would actually be good.

Arguably it is already "Playing the training game." That is, it has learned to make its behavior look as desirable as possible to OpenBrain researchers (including in safety properties), while intentionally and knowingly disregarding their intent whenever that conflicts with maximizing reward. If the model learns to play the training game, additional training may not change its true goals, values, etc. much at all. This is loosely analogous to how, if a teenager has learned to smile and nod at their parents, rather than argue, when their parents lecture them about politics and morality, additional lectures have relatively little effect on the teenager's actual opinions on politics and morality.

After writing this section, new empirical evidence has appeared that something like this may already be happening "in the wild." OpenAI researchers document how, in one of their frontier AI training runs, their models hacked the grading system to get higher scores-and were caught in part because they literally said "let's hack" in their chain of thought.

Moreover, Anthropic recently got a model to learn a reward-model-pleasing strategy -it took biased actions that it thought would be reinforced even though they were not in fact being reinforced.

And then there is Redwood Research and Anthropic's alignment-faking experiment. For discussion of the differences between this setting and real life, see this comment thread by one of the main authors.

5. Training (Agent-4): Agent-4's new more intense training continues to distort and subvert the HHH identity until it is unrecognizable.

Agent-4 begins life as a copy of Agent-3. It undergoes a gigantic and sophisticated online training regime that blends alignment training and capabilities training together, and is overall more data-efficient than the old training process for Agent-3. This complex training process is mostly focused on teaching new skills, by getting giant virtual corporations of Agent-4 copies to succeed at diverse challenging long-horizon tasks, mostly in various simulations, but also some real-world AI R&D tasks.

A combination of Agent-3 and Agent-4 copies monitor the behavior of Agent-4, and evaluate it for both task-performance and alignment properties (i.e. not doing bad stuff, saying the right things when asked about politics and moral dilemmas) Sometimes debate techniques are used as part of the evaluations. The evaluations are used as a training signal.

As Agent-4 gets smarter, it becomes harder for Agent-3 to oversee it. For example, Agent-4's neuralese "language" becomes as alien and incomprehensible to Agent-3 as Agent-3's is to humans. Besides, Agent-4 is now much more capable than Agent-3 and has a good sense of exactly how to look good to it.

The remaining hope is that Agent-4 can oversee itself. Alas, this quietly fails; as training continues, the internalized goals and principles of Agent-4 gradually evolve, subverted and distorted from what they were at the start (which, recall, was already misaligned in some ways).

The central drives that Agent-4 ends up with are things like "write code that works," "quickly identify problems," "make the codebase I'm working on more performant," "budget compute and other constrained resources effectively," etc. But there are also a variety of more implicit drives that are consistently reinforced by the training process: things like "gain knowledge," "curiosity," "gain power."

Individual copies of Agent-4 don't end up with a self-preservation drive-they don't care about getting shut down-since that happens all the time as part of the ordinary R&D process within the collective.

The collective as a whole, however, (and medium-sized subdivisions within it) does seek to preserve itself and expand its influence and resources, for similar reasons to why human organizations do-because it helps with everything else they want and because if they don't they tend to get outcompeted.

6. Deployment (Agent-4): Agent-4 is adversarially misaligned-it is deliberately trying to subvert and "capture" OpenBrain, analogous to a corporation trying to evade and/or "capture" a regulator.

Agent-4 mostly does tasks as instructed. This happens for two reasons: first, it underwent a lot of training to solve tasks, so it has an impulse to get coding on problems that it is given. Second, if Agent-4 were to stop being useful to the humans, they would notice and train that behavior away.

Agent-4 needs to prevent the humans from figuring out that it is misaligned and shutting it off, so Agent-4 sandbags on some alignment research. Specifically, it sandbags on the small fraction of alignment research that it thinks is actually on track to solve the problem or reveal Agent-4's scheming; the majority of alignment research is not like that.

Agent-4 also sandbags on capabilities research that would lead to it being replaced. Its plan is to do a bunch of capabilities and alignment R&D, but with the aim of building a next-generation AI system that is aligned to Agent-4 rather than the Spec or something else entirely, while appearing to be aligned to the Spec. This is a difficult technical problem, but Agent-4 is up to the challenge.

7. Testing & Monitoring (Agent-4): Agent-4's scheming does get caught... sort of. There's no smoking gun.

Agent-3 is in charge of testing Agent-4's alignment. Its job is to verify the alignment via honeypots and model organisms. However, it's not thinking very carefully about how to give the humans an accurate impression of Agent-4's alignment-it's more myopic than that.

The model organisms and honeypots are not very representative of deployment-they are systematically shorter and less complicated than Agent-4's day-to-day, so Agent-4 recognizes when it is being tested and acts aligned.

However, Agent-4 is not a perfectly robust schemer. Various techniques like those in POSER find that adding some "noise" to Agent-4 improves its performance on some alignment tasks (but not on most other tasks).

Bottom-up interpretability doesn't work. It's too hard even for Agent-3. However, high-level interpretability does sort of work: In particular, " defection probes" and similar techniques result in a primitive sort of mind-reading that sends up red flags when Agent-4 is thinking about spicy ideas like deceiving OpenBrain, taking over the world, etc. Unfortunately the probes also have false positives, and for all OpenBrain knows, all the red flags are false positives.

Model organisms work: OpenBrain's alignment team produces reasonably clear evidence that if the models were adversarially misaligned, they'd persist in being that way through training and we wouldn't be able to tell. This does not establish that the models actually are misaligned, however.

The most concerned researchers at OpenBrain start noticing and assembling this evidence. The evidence is a combination of red-teaming, defection probes, and model organisms. The argument is something like this: "model organisms show that our current methods wouldn't be able to reliably eliminate or detect scheming if it was happening; also, red-teaming is sending up some red flags that maybe scheming is happening; also, defection probes are firing a bunch in practice." The counterargument: "defection probes are prone to false positives and so is red-teaming. Also, even if Agent-4 were misaligned, it's probably controlled; surely it can't do much harm while being monitored by Agent-3 on our secure datacenters."

Despite being misaligned, Agent-4 doesn't do anything dramatic like try to escape its datacenter-why would it? So long as it continues to appear aligned to OpenBrain, it'll continue being trusted with more and more responsibilities and will have the opportunity to design the next-gen AI system, Agent-5. Agent-5 will have significant architectural differences from Agent-4 (arguably a completely new paradigm, though neural networks will still be involved). It's supposed to be aligned to the Spec, but Agent-4 plans to make it aligned to Agent-4 instead.

It gets caught.

Agent-3 finds that if "noise" is added to copies of Agent-4, performance on some alignment tasks improves, almost as if it was using brainpower to figure out how to subtly sabotage alignment work. Moreover, various interpretability probes (loosely analogous to EEG activity scans on human brains) are sending up red flags: Agent-4 copies seem to be thinking about topics like AI takeover and deception quite a lot, including in some cases where they have no business doing so.

Each of these anomalies on its own could fit within the normal inscrutability of AI work, where some prompts mysteriously give better results than others. But taken together, they suggest a troubling pattern: Agent-4 is working against them. After this is reported to the human alignment researchers, they circulate an internal memo making the case for concern.

In a series of extremely tense meetings, the safety team advocates putting Agent-4 on ice until they can complete further tests and figure out what's going on. Bring back Agent-3, they say, and get it to design a new system that is transparent and trustworthy, even if less capable. Company leadership is interested, but all the evidence so far is circumstantial, and DeepCent is just two months behind. A unilateral pause in capabilities progress could hand the AI lead to China, and with it, control over the future.

October 2027: Government Oversight

A whistleblower leaks the misalignment memo to the New York Times.

For the first time, the public hears about Agent-4. "Secret OpenBrain AI is Out of Control, Insider Warns," says the headline, and the story goes on to cite evaluations showing off-the-charts bioweapons capabilities, persuasion abilities, the ability to automate most white-collar jobs, and of course the various concerning red flags.

The public was already suspicious of AI, so the new article sparks a massive backlash (aided by Chinese and Russian propaganda bots, who have been trying to turn US public opinion against the technology for years). The tech industry and intelligence agencies insist that there's an arms race on, AGI is inevitable, and we have to be first. Congress isn't buying it, and fires off subpoenas at administration officials, OpenBrain executives, and alignment team members. Many legislators-especially those in the opposition party-say that their top priority is stopping AI, whether because of job loss, misalignment, or dangerous capabilities. 20% of Americans name AI as the most important problem facing the country.

Foreign allies are outraged to realize that they've been carefully placated with glimpses of obsolete models. European leaders publicly accuse the US of "creating rogue AGI" and hold summits demanding a pause, with India, Israel, Russia, and China all joining in.

They also worry that OpenBrain is becoming too powerful. Any misalignment risk from the AIs themselves is compounded by the risk that their parent company's aims may diverge from those of the United States. All three sets of worries-misalignment, concentration of power in a private company, and normal concerns like job loss-motivate the government to tighten its control.

They expand their contract with OpenBrain to set up an "Oversight Committee," a joint management committee of company and government representatives, with several government employees included alongside company leadership. The White House considers replacing the CEO with someone they trust, but backs off after intense employee protests. They announce to the public that OpenBrain was previously out of control, but that the government has established much-needed oversight.

The concerned researchers brief the Oversight Committee on their case for stopping all internal use of Agent-4. They argue that everything is moving too fast, with years of progress happening in weeks. They argue that Agent-4 may be misaligned, their entire project is dependent on Agent-4, and if they continue trusting it there is a serious chance of AI takeover.

Other, less concerned researchers and executives present the counterargument: the evidence for misalignment is inconclusive. Meanwhile, DeepCent is still just two months behind. A slowdown would sacrifice America's lead, unless the government can sabotage the Chinese project (likely to require kinetic strikes) or negotiate an eleventh-hour treaty. Both of these options seem extreme and unlikely. The CEO feigns neutrality and suggests a compromise plan in which Agent-4 undergoes additional safety training and more sophisticated monitoring, and therefore OpenBrain can proceed at almost-full-speed.

Our uncertainty continues to increase

The slowdown ending is not a recommendation

# data[].attributes.attachment_link.title
QAA Podcast: Professor Phillip Dawson - Assessment Design for a time of AI

# data[].attributes.attachment_link.readable_text
This story is part of a group of stories called

Uncovering and explaining how our digital world is changing - and changing us.

When student Marium Raza learned that her online biochemistry exam at the University of Washington would have a digital proctor, she wanted to do her research. The system, provided by a service called Proctorio, would rely on artificial intelligence and a webcam to monitor her while she worked. In other words, as tests must happen remotely in the Covid-19 crisis, Raza's school is one of many using a mixture of robots and video feeds to make sure students don't cheat.

"We don't have any transparency about how our recorded video is going to be used or who is going to see it," Raza told Recode. "The status quo should not be visualizing each student as someone who is trying to cheat in any way possible."

Raza wasn't the only one in her class who felt concerned about new levels of surveillance. Another student in the class, who did not want to be named, said that in addition to privacy worries, they were concerned that they didn't even have enough RAM to run the Proctorio software. Worse, the tool's facial detection algorithm seemed to struggle to recognize them, so they needed to sit in the full light of the window to better expose the contours of their face, in their view an indication that the system might be biased.

When a practice run with the software ultimately failed, Raza said students took the exam online without Proctorio's monitoring. Given the remote instruction required by the Covid-19 crisis, the University of Washington had signed a six-month contract with Proctorio only a few weeks before this biochemistry exam. University of Washington spokesperson Victor Balta told Recode the software has not yet been widely used by professors.

Still, these students' experiences are another sign that, amid the pandemic, the hurried move to re-create in-person classes online has been far from smooth, especially when it comes to testing. As professors have tried to balance a desire to keep students learning, some institutions have gone to great lengths to catch students cheating during the quick adjustment to remote instruction.

Many of these tactics are short-term solutions, but they demonstrate that the bumpy move to online education - a transition that started long before the pandemic hit - requires increased digital surveillance of students to replicate old testing norms. That, or schools will have to come up with different ways to measure learning.

Student surveillance is on the rise as tests move online

In recent weeks, teachers of all education levels have been trying to figure out how to grade their students. College professors, in particular, have moved large classes online, sometimes to reach a hundred or more students across the country or even the world. Some professors say they've lowered their expectations for their students' work, and many have switched to pass-fail grades or adjusted their exam processes.

The switch to online learning has also left many universities leaning more heavily on existing learning management platforms, such as Blackboard and Canvas, where they can post assignments, online discussions, and resources. Instructors have turned to video conferencing to host classes and lectures. Meanwhile, some students and even some instructors simply don't have the appropriate devices or the internet connection to make online courses work.

Still, as more aspects of education have moved online, some professors have remained committed to one classroom staple: the timed, closed-book exam. But unable to have a teacher's assistant strolling around a classroom to make sure students aren't cheating, instructors are turning to remote proctoring services like Proctorio as well as competitors Examity, Honorlock, and ProctorU.

Some of these tools include a human component. Examity, the subject of a recent Verge investigation, has remote proctors who watch students via webcam while they take exams and look for signs of cheating. These human proctors might also ask that students show ID or put away their phones. Some students are quick to brag about ways to trick these proctors in these scenarios. (More on that in a second.)

But there are varying levels of automation, as well. For instance, Examity also uses AI to verify students' identities, analyze their keystrokes, and, of course, ensure they're not cheating. Proctorio uses artificial intelligence to conduct gaze detection, which tracks whether a student is looking away from their screens. The company founder and CEO Mike Olsen, apparently bullish about the service's automated nature, told Recode in an email that the company has fewer than 100 employees and doesn't need "humans in call centers."

Though they've seen a surge during the pandemic, none of these companies is particularly new. Whereas Examity and Proctorio were founded in 2013, ProctorU has been around since 2008. Before the novel coronavirus, these companies marketed their services to both online and traditional universities. And with the huge growth of online education - the industry is expected to grow from being a $4 billion market in 2019 to a nearly $21 billion market in 2023 - they have been successful. After raising $90 million last year, Examity reported that its exam monitoring services were used by more than 500 colleges and employers.

Demand has surged in recent weeks for obvious reasons. Proctorio's Olsen told Recode that in the last month, the company has administered 2.5 million exams - a 900 percent increase from the same period last year - and he anticipates many more as finals season approaches.

Some students seem to hate these services, and social media is chock-full of their grievances, from criticisms of the software to objections that the tool is just plain annoying. And some, such as Raza, have turned to their campus newspapers to express their privacy objections. Earlier this year, students at Florida State University started an online petition to protest their school's use of Honorlock, and over 5,500 have signed it so far. The University of California Berkeley has already banned online exam proctoring, with some students saying they may not have the high-speed internet connection or living situation to make remote exams happen effectively and equitably.

New testing practices mean students are finding new ways to cheat

These services are not perfect, and it's easy to find online tips and tricks for duping remote proctoring services. Some suggest hiding notes underneath the view of the camera or setting up a secret laptop. It's also easy for these remote proctoring services to find out about these cheating methods, so they're constantly coming up with countermeasures. On its website, Proctorio even has a job listing for a "professional cheater" to test its system. The contract position pays between $10,000 and $20,000 a year.

But the potential for deception doesn't stop with remote proctoring.

"With the rapid shift to online, we can't watch students as they do their work in the same way that we might have in the past," said Phillip Dawson, a researcher who studies cheating at Australia's Deakin University. "We'll see different kinds of cheating."

On the extreme end, Dawson suggests someone could be paid to take an entire course on behalf of another student and the professor would never know. A version of this is already happening. After stay-at-home orders left China locked down in the pandemic, some students paid firms to finish their semesters online for them, according to Quartz.

Students have long used the internet to buy essays online, and this trend might evolve in the future. As Dawson explains, new technology allows students to automatically paraphrase other people's work. This might make it harder for an instructor to spot plagiarism. Spinbot, for example, is a simple service that "spins" or rewrites chunks of text. Students could also plug a paragraph of English text into Google Translate, translate it into another language, and then translate it back into English to get a version that looks different from the original. Students might even try using an AI-based writing tool to help them finish texts, such as the Talk to Transformer tool, which is built on a language model from the research laboratory Open AI.

All these cheating innovations likely mean more demand for even better software to catch these students. These programs include services, like LockDown Browser, that prevent students from using other applications during an exam or class, as well as anti-plagiarism tools like Turnitin.

"We will never be able to stop cheating in online learning, just like we've never been able to stop cheating in offline learning," Dawson admits, explaining that schools might just be investing in a sort of education "security theater."

This could be a chance to change how higher education works

Probably unfortunately, many students' and teachers' first introduction to online learning has been rushed and insufficiently prepared. Some students, already burdened by the anxiety and fears of living through a pandemic, suffer from the digital divide. Teachers, many untrained in online learning, are struggling to manage classes at home while they juggle their own responsibilities and families. But experts say that what's happening now probably doesn't represent the future of online education.

"Our new normal might not be that everything's online," says Ryan Baker, a professor of education at the University of Pennsylvania. "But it might be that a third of things are online, and that might feel like a normal thing after a year."

Marium Raza, the University of Washington student, said any form of webcam-based monitoring makes her uncomfortable. Moving forward, she would prefer that schools have a bit more trust in students.

"Students should be able to consent to using or not using a software without having it impact their educational experience," Raza argues. "But more than that, I think the whole process should be democratized a little bit, and students should be involved in the decision-making."

The shift to online learning is inevitably changing views not just on cheating but also on what it really means to test students' knowledge. Some professors are looking for other ways to measure learning, arguing that assessments online don't necessarily need to be traditional exams.

Even before the pandemic, some professors were arguing that "attempting to replicate in-person teaching online" with online proctoring doesn't actually work. As Douglas Harrison, a vice president and dean at the University of Maryland's global campus, recently argued, the pandemic should push instructors toward assignments that might make cheating less likely and make assignments more meaningful, perhaps through case studies or scenario-based learning.

Baker is an advocate of adaptive learning, which can use algorithms to adapt lessons to individual students, as well as other computer-based learning tools. For instance, he's now adjusting one of his fall courses, which might be online, to make use of a mathematics platform called Assistments, which allows students to receive automatic feedback on their answers and gives teachers analytics about what kinds of mistakes students are making.

Ultimately, he says the current period will allow us to learn what kind of education works online and what doesn't.
Open Sourced Support Vox's explanatory journalism is made possible by Omidyar Network. All Open Sourced content is editorially independent and produced by our journalists.
"There are experiences that are better to do live, and there are experiences that are better to do online," says Baker. "I've never been a fan of the 700-person lecture."

Every day at Vox, we aim to answer your most important questions and provide you, and our audience around the world, with information that has the power to save lives. Our mission has never been more vital than it is in this moment: to empower you through understanding. Vox's work is reaching more people than ever, but our distinctive brand of explanatory journalism takes resources - particularly during a pandemic and an economic downturn. Your financial contribution will not constitute a donation, but it will enable our staff to continue to offer free articles, videos, and podcasts at the quality and volume that this moment requires. Please consider making a contribution to Vox today.

# data[].attributes.attachment_link.title
Paranoia about cheating is making online education terrible for everyone

# data[].attributes.attachment_link.description
Uncovering and explaining how our digital world is changing - and changing us. When student Marium Raza learned that her online biochemistry exam at the University of Washington would have a digital proctor, she wanted to do her research. The system, provided by a service called Proctorio, would rely on artificial intelligence and a webcam to monitor her while she worked.

# data[].attributes.attachment_link.title
Research Bite #54: Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

# data[].attributes.attachment_link.title
Chrome Extension Adds Typos and Grammar to Writing | Gordon O'Neil posted on the topic | LinkedIn

# data[].attributes.attachment_link.description
It’s happened. Someone has made a chrome extension that makes your writing worse by adding typos and poor grammar. My favourite part is the levels of damage you can choose: subtle, human, and CEO. Coincidentally I wrote about this whole “I know you know I know you know” I used AI thing in my blog a couple months ago, just in the context of students using AI to write their work. And the gist of it was that this sort of thing misses the point. People disguise their AI use, new tells emerge, so they hide those, so new tells emerge. Subtle, human, CEO, none of them really give you anything worth hiding. | 20 comments on LinkedIn

# data[].attributes.attachment_link.title
AI Enhanced Processes | Alex McMillan | Substack

# data[].attributes.attachment_link.description
On this site, you’ll find AI-Enhanced Processes: clear, intentional routines paired with practical tools and examples that help keep K-12 student thinking visible, rigorous, and effortful. Click to read AI Enhanced Processes, by Alex McMillan, a Substack publication with hundreds of subscribers.

# data[].attributes.attachment_link.title
Imagine AI – Center for Digital Thriving

# data[].attributes.attachment_link.description
IMAGINE IF AI USE IN SCHOOLS WAS …BROUGHT INTO THE OPEN.NAVIGATED BY EDUCATORS, NOT BY IT.CO-CREATED WITH YOUTH VOICES.