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Dr. Aviva Legett - AI Is Now Fundable In Higher Ed—But Only With Real Governance

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Dr. Aviva Legett - AI Is Now Fundable In Higher Ed—But Only With Real Governance

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AI Is Now Fundable In Higher Ed—But Only With Real Governance

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AI is fundable and reviewable in higher ed. This article explains the new governance evidence accreditors and DOE require, with real campus case studies.

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Harmonize: Here’s How to Make Your Assessments AI Resilient (while still supporting student learning)

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Harmonize: Here’s How to Make Your Assessments AI Resilient (while still supporting student learning)

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Here’s How to Make Your Assessments AI Resilient (while still supporting student learning)

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These strategies make your assessments more resilient in the face of AI while also supporting best practices for learning.

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Generative AI in Higher Education: Innovation Strategies for Teaching and Learning (2024 book)

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Generative AI in Higher Education: Innovation Strategies for Teaching and Learning (2024 book)

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This book explores the changing role of educators in the light of rapid technological advances in higher education.

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UCSC Legal Research Center - Generative AI Detection Tools

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UCSC Legal Research Center - Generative AI Detection Tools

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Guides: Generative AI Detection Tools: The Problems with AI Detectors: False Positives and False Negatives

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A guide for instructors on the use of generative AI detectors. This guide is not an endorsement of any particular tool. AI detectors are problematic and not recommended as a sole indicator of academic misconduct.

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AI is not the enemy of learning — outdated education standards are

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AI is not the enemy of learning — outdated education standards are

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"Rather than viewing AI as a shortcut, educators should recognize it as a valuable tool to help students engage more deeply with the learning process. And until that changes, students will be left to fend for themselves in an environment that often prioritizes results over the learning journey."

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OPINION: AI is not the enemy of learning — outdated education standards are

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Technology is evolving at an unprecedented rate, but can education keep up with this rapid pace of change? As new tools and innovations emerge, the question becomes whether traditional education systems are equipped to adapt and fully embrace these advancements.

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Dale & Jennifer Harris (RNH) - US District Court of MA Case

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Dale & Jennifer Harris (RNH) - US District Court of MA Case

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From Innovation To Impact: How AI is Reshaping Higher Learning

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From Innovation To Impact: How AI is Reshaping Higher Learning

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https://www.insidehighered.com/sites/default/files/2025-11/Slides%20From%20Innovation%20To%20Impact%20How%20AI%20is%20Reshaping%20Higher%20Learning.pdf

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done. Zoom is a publicly traded company headquartered in San Jose, CA.

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Five lessons from a conversation about AI and the future of higher education.

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Five lessons from a conversation about AI and the future of higher education.

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Inside Higher Ed | Higher Education News, Events and Jobs

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Lessons From a Conversation About AI, Future of Higher Ed

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Five lessons from a conversation about AI and the future of higher education.

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U Delaware: Reimagining PedAIgogy Workshop Series

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U Delaware: Reimagining PedAIgogy Workshop Series

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https://urldefense.com/v3/__https://docs.google.com/presentation/d/1XYS_fwG_8Sf_K9Uwj58cFfEAVtnkXmHib5XlC3FND0I/edit?usp=sharing__;!!K1w2cQG9r60!wpJzKCx1OzjMZYYWXwiviGEpruOxUrirdnUROdnaNXEX9scixWWgtlnyXobrjxxX1kZR539mYAAxrh9Lq2beKw$https://urldefense.com/v3/__https://chatgpt.com/g/g-688930a9933081919a2c81aca4e3ac99-ai-privacy-ethics-strategist__;!!K1w2cQG9r60!wpJzKCx1OzjMZYYWXwiviGEpruOxUrirdnUROdnaNXEX9scixWWgtlnyXobrjxxX1kZR539mYAAxrh9UBDYeiQ$

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GenAI Survey for Higher Education Assessment Professionals

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GenAI Survey for Higher Education Assessment Professionals

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GenAI Survey for Higher Education Assessment Professionals

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Course and Assignment (Re-)Design (AI-Infusion Decision Tree)

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Course and Assignment (Re-)Design (AI-Infusion Decision Tree)

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We cannot fully anticipate how GenAI will impact every course or assignment at U-M. We will need to approach GenAI with an experimental mindset, draw on what we know, prioritize high-impact pedagogical strategies, and plan for incremental, sustainable instructional development. High-impact pedagogy includes collaborative learning, interactive learning, higher-order thinking, service- or community-based learning, research, authentic learning and assessment, and writing-intensive courses. Writing-intensive courses present particular benefits, opportunities, threats, and risks.

To begin this process, we recommend that instructors practice using GenAI tools relevant to their disciplines, interests, and pedagogies-before the start of fall term.

In preparation for fall term, instructors should evaluate several aspects of their courses:

What are the course objectives and rationale for them? Can GenAI be used to meet any of the objectives?

Are there new learning objectives in the areas of knowledge, skills, or values about GenAI that students need to meet? Will students have equitable access to GenAI for these objectives?

What tasks do students need to complete to demonstrate they meet the learning objectives?

How will learning be assessed?

Do the objectives, tasks, or assessments present problems with respect to equity, inclusion, diversity, or accessibility? If so, can they be adjusted to ensure fairness and inclusion?

The course design should include some parts that cannot be completed satisfactorily (solely) by GenAI tools, unless GenAI skills and values are the primary learning objectives. GenAI offers us an opportunity to rethink learning objectives as well as assessment methods. Several principles of effective pedagogy should guide us in adjusting courses to GenAI environments:

Larger component of technology-free in-class assignments
Including low-stakes writing, programming logic, etc. may be possible, while recognizing that in-class assignments can create inequities for some students with disabilities and English-language learners, unless appropriate accommodations are offered.

Focus on higher-order thinking
Require students to develop their capacity for creating, evaluating, analyzing, and applying (what they learned to new contexts or problems)

Emphasize the learning process over the product when appropriate
Assign scaffolded tasks developed through several stages (low-stakes writing, outlines, proposals, outlines, drafts, peer review, revision)

Prioritize authentic instruction and assessment
Assign tasks that address real-world problems or simulate real-world tasks; require students to use disciplinary theories and methods in innovative ways; ask students to address complex questions that have no easy or right answer; provide formative feedback that improves students' performance

Require accurate and verifiable citation of sources
Have students include links to the sources they use in a project (GenAI cannot reliably do this)

Add metacognitive exercises to assignments
Assign reflections about how formative feedback is understood and what was revised in response to feedback

Teach academic integrity
In designing assignments, we should explain the purpose of the task to foster motivated learning. The purpose should align with learning objectives in the areas of knowledge, skills and values:

what do students need to learn and why-what the value of the learning is for them now and/or in the future?

What skills will students gain from using AI, what knowledge they will need to apply.

Instructors are encouraged to try completing assignments using GenAI tools before distributing assignments to students. GenAI output can productively be used in class as a prompt for discussion about what technology does well and not so well, as well as the potential social risks and harms implicit in the output (e.g., racial bias, misinformation).

The following decision tree may be used to evaluate assignments in relation to GenAI environments:

IMAGE CREDIT: Frederique Laubepin

Instructors may find it useful to consider the principle of "transparent assignment design" as they review the relationship between course learning objectives and assignments. A transparent assignment template created by Mary-Ann Winkelmes at the University of Illinois offers a concise and concrete example.

Instructors may decide to assess learning with a wider range of assignments than they employed before sophisticated GenAI tools became widely available. The following strategies may prove useful.

Strategies for Engagement and Assessment

In line with best practices for teaching and learning, strategies for engaging and assessing students beyond recall and procedural, superficial understanding are effective pedagogical measures for rising above GenAI tools. Diverse teaching strategies align with diverse approaches to assessment. The following chart offers suggestions for instructors to consider as they redesign courses for GenAI environments.

Active, experiential, and project-based learning sequences similar to those encountered in the discipline

In-class polls, products of peer collaboration, self-assessment

Flipped classrooms to develop interactive learning, peer collaboration, and problem solving skills

In-class polls, products of peer collaboration

Reflections on learning and research processes

Assign a description of research methods, evaluation of sources, explanation of how sources contributed to students' thinking, and discussion of problems encountered and solved in the process.

Evaluate on the basis of completeness.

Live debates, oral presentations, role playing

Evaluation rubric co-created with students before the performances.

Evaluate on the basis of completeness.

Multimodal compositions, such as infographics, podcasts, drawings, websites, videos, alternative text creation for videos and images

Evaluation rubric co-created with students during scaffolded composition process

Authentic group, individual, and self-assessments that require critical and creative thinking

Social annotation of reading

Peer review of annotation quality

Requirements for students to contextualize information, make connections in written work to their lives, current events, and earlier course concepts or lectures, and/or incorporate required readings, videos, images, case studies, or datasets

Evaluate on the basis of completeness and successful integration/synthesis of course materials, concepts, theories, or methods

Poster displays and discussions

Peer review according to a rubric co-created with students during the learning process

Grading standards should be explicit when assignments are distributed and should prioritize higher-order thinking, veracity, critical and creative thinking, and learning processes over mechanical or grammatical correctness. The rationale for these priorities is that developing the human capacity for sentience, creativity, and reasoning aligns with the social value of higher education, while "correctness" (within narrow limits) can increasingly be accomplished by machines.

Integrating AI tools into Course Design

Teaching in an AI-augmented world will demand a reevaluation of the course design and pedagogical approaches. AI can be incorporated into a course in several ways. Examples include providing personalized learning experiences, predictive analytics to identify student progress towards learning goals, and automated grading systems. While integrating AI, it is important to critically examine:

Appropriate use of AI
Ensure that AI is used in a way that supports the learning objectives of the course.

Necessity of AI
While AI can improve various aspects of teaching and learning, it is crucial to discern if its implementation is necessary for a particular task or if a non-technological approach would be equally or more effective.

Pros and Cons of AI
Understand the benefits, such as increased efficiency and personalized learning, against the potential risks and drawbacks such as privacy issues, technology dependency, or reducing human interaction.

Avoiding the Tail Wagging the Dog Scenario
It is essential to ensure that AI serves as a tool to achieve educational objectives and not the other way round where the curriculum is altered to fit the AI tools.

Book Guide - Using Chatbots in Classroom Assignments and Activities
A TTC-created guide that highlights ideas for using chatbots in classroom assignments and activities. The guide is based on the ChatGPT Assignments to Use in Your Classroom Today book from the University of Central Florida.

Skills and Competencies for an AI-Augmented World

The future workforce will necessitate a unique blend of skills and competencies to thrive in an AI-driven environment. Accordingly, regardless of the field of study students should be exposed to the following aspects of GenAI:

Digital Literacy
Students should understand how AI systems work and appreciate the intricacies of machine learning, data analysis, and algorithm design.

Ethical Understanding
As AI systems become more complex, issues around privacy, bias, and ethics will be prominent. Students need to be aware of these ethical considerations and equipped with the skills to make informed decisions based on their values.

Critical Thinking and Problem Solving
While AI can automate many tasks, the ability to think critically and solve complex problems is something that cannot be replicated in the near future by machines. The cognitive dimension of learning is more important than ever, given the necessity of evaluating, fact-checking, and verifying AI output, as well as protecting against and mitigating the risks and harms of AI. These will remain key skills in the near-future workforce.

Adaptability and Lifelong Learning
AI is a rapidly evolving field. Therefore, the ability to adapt to new technologies and a commitment to lifelong learning will be crucial.

In conclusion, while AI can greatly augment the teaching and learning process, it is critical to maintain the delicate balance between leveraging technology and nurturing the unique human skills that AI cannot replicate. Doing so can prepare our students for an AI-augmented world without compromising on the essence and social value of education.

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We cannot fully anticipate how GenAI will impact every course or assignment at U-M. We will need to approach GenAI with an experimental mindset, draw on what we know, prioritize high-impact pedagogical strategies, and plan for incremental, sustainable instructional development.

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Book Guide - Using GenAI in Classroom Assignments and Activities

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Book Guide - Using GenAI in Classroom Assignments and Activities

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Book Guide - Using GenAI in Classroom Assignments and Activities (U-M TTC)

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FL-IDN: How AI is Revolutionizing Accessibility + A 3-Step Process for Integrating A.I. in Teaching

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FL-IDN: How AI is Revolutionizing Accessibility + A 3-Step Process for Integrating A.I. in Teaching

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Presentation #1: https://drive.google.com/file/d/15gdkwEZHb2qbYCFvmudayEptO17Mcpyk/view?pli=1 Presentation #2: https://drive.google.com/file/d/1nPtJ4ghA04svbyS4p_GsImhNI1NrvvSq/view

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AI in Educational Development (Masland, App State & Palmer, UVA)

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AI in Educational Development (Masland, App State & Palmer, UVA)

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AI in Educational Development - Facilitators Guide

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A VOICE AND A FORCE FOR LIBERAL EDUCATION. AAC&U advances the vitality and democratic purposes of undergraduate liberal education.

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Hatch Webinar - Idea to Done: Practical AI for Impossible Projects

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Hatch Webinar - Idea to Done: Practical AI for Impossible Projects

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q@nmF!7W

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

# data[].attributes.attachment_link.description
Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done. Zoom is a publicly traded company headquartered in San Jose, CA.

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 Navigating Dyslexia with AI Technologies

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 Navigating Dyslexia with AI Technologies

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Technology Solutions That Drive Education

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ISTELive 24: Navigating Dyslexia with AI Technologies

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Educational technology accommodations support neurodivergent learners, but only when they’re in use. One student shares what teachers need to do.

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AI is a game changer for students with disabilities. Schools are still learning to harness it

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AI is a game changer for students with disabilities. Schools are still learning to harness it

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AI is a game changer for students with disabilities. Schools are still learning to harness it

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Artificial intelligence holds the promise of helping countless other students with a range of visual, speech, language and hearing impairments to execute tasks that come easily to others.

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Zhou et al (2025) - Critical Thinking, Self-Efficacy, Creativity, Engineering Students

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Zhou et al (2025) - Critical Thinking, Self-Efficacy, Creativity, Engineering Students

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Zhou et al (2025) - The chain mediating role of critical thinking and AI self-efficacy in GenAI usage competence and engineering students' creativity

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Resisting, Refusing, Reclaiming, Reimagining: Charting Challenges to Narratives of AI Inevitability

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Resisting, Refusing, Reclaiming, Reimagining: Charting Challenges to Narratives of AI Inevitability

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Resisting, Refusing, Reclaiming, Reimagining: Charting Challenges to Narratives of AI Inevitability

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Increasingly, artificial intelligence (AI) adoption is widely framed as inevitable; a destiny which cannot be avoided. In the context of AI, “inevitability” is often used to argue for the need to adopt AI solutions, or to adapt and prepare for speculative AI developments. With soaring investments into AI technologies and infrastructure, unabating coverage of AI stories in the media, and growing enthusiasm from governments, it is unsurprising that AI is widely perceived as inevitable. However, this perception is shaped by narratives that diminish our agency as humans, as they present technology as advancing independently from human activity and interests. In this paper, we outline instances of individuals and collectives actively defying narratives about the inevitability of AI. We propose a taxonomy of such efforts, which are instances of Resisting, Refusing, Reimagining and/or Reclaiming AI. The taxonomy serves as a conceptual framework for those challenging the power dynamics underpinning AI technologies, and illustrates that there are many ways individuals and collectives can influence how technology is developed and used. The taxonomy is also a call to unity for a collective effort; a plurality of resistances which can define and build a future on terms not dictated by techno-determinist ideologies and agendas.

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NPR - 1 in 5 high schoolers has had a romantic AI relationship, or knows someone who has

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NPR - 1 in 5 high schoolers has had a romantic AI relationship, or knows someone who has

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1 in 5 high schoolers has had a romantic AI relationship, or knows someone who has

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A national survey of students, teachers and parents shines a light on how the AI revolution is playing out in schools – including when it comes to bullying and a community’s trust in schools.

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CHE (2023) - The Myth of the Digital Native_Adobe_ResearchBrief

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California attorney fined $10K for filing appeal with fake legal citations generated by AI

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California attorney fined $10K for filing appeal with fake legal citations generated by AI

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California attorney fined $10K for filing appeal with fake legal citations generated by AI

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A California attorney must pay a $10,000 fine for filing an appeal full of fake quotations generated by artificial intelligence tool ChatGPT.

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Halfway to its 2030 net-zero goal, Apple faces slow and hold-out suppliers, a tariffs scramble, and an AI race that could profoundly impact eco-friendly ambitions.

Here's a simple question: Is the current top iPhone better for the environment than the top iPhone was five years ago?

Let's take the iPhone Pro series. If we're looking at recycled and renewable materials, it's an easy yes. Compare the iPhone 11 Pro, released in September 2019, with the iPhone 16 Pro, released in September 2024, and there has been good progress-from a few smaller components and packaging to now at more than 25 percent of the whole phone. There's work to do, of course, but that's what you'd expect at Apple's halfway point to its 2030 goal of carbon neutrality.

But what the iPhone is made out of is only one part of the picture. Take a look at the carbon emissions for each iPhone's life cycle, including everything from materials to the electricity for manufacturing, transportation and charging, and things get more complicated. The AI race is only compounding the problem.

In 2020, the year Apple set out its net-zero climate goals, per-phone emissions were 82 kilograms CO2E (carbon dioxide equivalent) for the iPhone 12 Pro. After a sharp decrease to 69 kilograms in the iPhone 13 Pro, progress that Apple attributes to its Supplier Clean Energy Program, things start to slow-and then, with the iPhone 15 Pro, stall almost completely.

It's at this point that Apple changed its reporting in a way that makes it harder to understand year-on-year improvements-moving to a comparison versus a 2015 baseline of 92 kilograms instead. So when the iPhone 16 Pro arrived in 2024 with the same level of emissions as the 15 Pro, it was still positioned as having greenhouse gas emissions savings of 30 percent rather showing those savings at a relative standstill, year on year.

So while, yes, the current top iPhone is better for the environment than five years ago (and the figures for some of the base models show even greater improvements), most of that progress was made between 2020 and 2022. As Apple has pushed forward with AI and introduced emissions-heavy technological innovations to support it, the company has been left mostly treading water. All while, in contrast, 2023's Fairphone 5 managed to get its life cycle emissions down to just 42 kilograms. (Fairphone will release the figures for its newer Fairphone 6 in October.)

The wider context here, though, is that the house is on fire. Google's total greenhouse gas emissions increased an astonishing 48 percent between 2019 and 2023-in part due to its energy-guzzling AI data centers-and last year it dropped its carbon neutrality pledge (though it still claims it will be at net-zero emissions by 2030). Elsewhere, Microsoft's total emissions increased by 23 percent from 2020 to 2024, which it attributed to growth in AI and cloud expansion, Amazon's shipping and delivery emissions keep going up, and Samsung is running behind on its more limited net-zero aims.

As the kids say, the bar is in hell. At least Apple hasn't backtracked; Apple's still playing. But as Tim Cook doubles down on Apple's commitment to AI, can Cupertino keep up with its climate goals while pushing forward in its AI ambitions?

Slowing Progress

In a comment to WIRED, Apple didn't exactly acknowledge the challenges it's facing. "As data clearly show, Apple is on track to meet our ambitious goal to be carbon neutral across our entire footprint by 2030," it said.

"We've reduced our global emissions by more than 60 percent since 2015 and worked with our suppliers to bring nearly 18 gigawatts of clean energy online across our supply chain. We're laser-focused on continuing this progress with more collaboration and innovation in the years ahead."

However, at the September 2024 iPhone launch, Apple was pretty quiet about its sustainability progress. There were only a few mentions of the recycled materials used across its hardware and its packaging-a stark contrast to the five-minute chat with Octavia Spencer as Mother Nature that opened 2023's event.

Then, in April 2025, Reuters reported that Apple had chartered a cargo jet with a capacity of 100 tons out of Chennai airport in Tamil Nadu, India. It was the sixth chartered flight on the route since March, designed to beat Trump's tariffs and get an estimated 1.5 million iPhones into the United States, fast.

This might seem like a questionable move for a company striving for carbon neutrality, but Lena Chang, climate and energy program director at Greenpeace East Asia, says things like this are actually the least concerning issue facing Apple's 2030 pledge: "It's ironic to point out [the cargo jets], but transportation emissions are a small part of the supply chain."

Instead, Greenpeace points to other forces it believes are slowing Apple's progress. According to Apple's 2025 Environmental Progress Report, reductions in its total "Scope 3" manufacturing emissions-the indirect greenhouse gas emissions outside of the company's control but created due to its operations-slowed to a 13 percent decrease between September 2023 and September 2024 versus an equivalent 30 percent decrease from 2022 to 2023.

To explain this, Chang and her team are focused on what they see as the gap between Apple's commitments and those of its suppliers in Asia, producing an easily digestible ranking of the recent climate progress and commitments of the five major final assembly suppliers to Apple: Foxconn, Luxshare Precision, Wistron, Compal and Pegatron.

Of them all, only Wistron has commitments that align with Apple's ( 100 percent renewable energy by 2030), but the company hasn't released any data for 2024 as yet. Pegatron is only committed to 50 percent in the same time, with Foxconn and Compal not hoping to make 100 percent until 2040 and 2050 respectively. (WIRED requested comment from all five of these suppliers and received no reply.)

Chang points to the ambition of Apple's two Clean Energy Funds in the region, worth around $400 million, but wants to see stronger accountability from the company regarding its suppliers in mainland China and Taiwan.

"We are talking with some suppliers of Apple, and they actually complain that 'yeah, we see there's money there, but there's just not enough incentive for us to initiate a project,'" Chang explains. "We need suppliers to take more initiative, and we need stronger enforcement from Apple, whether it's monitoring on a year-to-year basis or strengthening its reporting mechanism to really push its suppliers to adopt more renewable energy."

This problem now stretches to India too, alongside Vietnam, as Apple scrambles to shift the assembly of the iPhones bound for the US, due to instability around global tariffs. Foxconn accounts for a large share of the local iPhone assembly in the region, but Tata Electronics, a subsidiary of Tata Group, is growing in importance after several big investments. (WIRED requested comment and sustainability data from Tata Electronics; the company said it cannot accommodate the request.)

"Tata Electronics itself, as a subsidiary, has not said anything yet on climate-anything significant," says Ashish Fernandes, CEO and founder of Climate Risk Horizons, a think tank covering India's energy transition. Tata Group has various climate commitments and was carbon neutral for its energy usage for a period, but Fernandes says it isn't clear how these impact the iPhone assembly operations-a lack of clarity he believes can't continue: "If you want to manufacture for Apple, there's going to be a lot of scrutiny."

It isn't alone. From the 14 suppliers that have manufacturing units for Apple in India, Climate Risk Horizons researcher Simran Kalra says, only three have specified their renewable energy share in India-and it isn't particularly good news.

FIH Mobile's 2024 ESG report showed a renewable energy ratio for its two plants in Tamil Nadu was 35.5 percent (FIH said it is not currently "a direct Apple supplier," but the company has previously worked with Apple), while Flex's was around 27 percent in both 2023 and 2024. As for Pegatron? Its 2022 and 2023 reports showed it used no renewable energy in India at all.

"India is not exactly a country where it's difficult to get renewables going," says Fernandes. "We've got a pretty big domestic renewables industry already. So it should be fairly simple for Apple to insist to its suppliers that they meet the bulk of it from renewables and 100 percent by 'X' date."

The AI Problem

However, it is the conflict between AI and climate goals that can be viewed as the most "vicious cycle," as UK-based analyst Jan Stryjak, who leads Counterpoint Research's work on sustainability, puts it.

"AI is hugely costly when it comes to energy usage and water. So a lot of companies are using AI to make their operations more efficient in order to use more AI, or to support more AI, which in turn requires them to be more energy and operationally efficient. Apple is no different."

At first blush, Apple Intelligence at least looks to be somewhat different. It aims to offer an on-device, privacy focused, perhaps more climate-friendly alternative to the cloud-based AI agents. For user requests that need larger models, Apple points to its creation of Private Cloud Compute, hosted on Apple silicon servers at the company's data centers, which are powered by 100 percent renewable energy.

But can Apple scale this 100 renewable infrastructure while competing in a field where it is lagging behind? According to Data Centre Map, Apple operates 16 of these data centers, with six more sites planned or under construction. For comparison, OpenAI partner and investor Microsoft has 120-plus data centers, Amazon runs 200-plus, and Google runs 66 with at least 30 more planned-the very expansions behind some of the biggest climate progress reversals.

Even as a smaller, more energy-efficient AI agent, Apple Intelligence still has power trade-offs-particularly when it leans on ChatGPT to answer some search questions in Siri, a partnership which Apple said late last year is not part of the Private Cloud Compute plan.

"A year ago, when they were talking about Apple Intelligence, it struck me how they were doing an 'all of the above,' right? They had their own thing but they could fall back onto ChatGPT," says Ben Lee, a professor at the University of Pennsylvania in the department of electrical and systems engineering, who has previously consulted for Meta and Google on sustainability.

"That suggests that they're not quite sure that the smaller models give them what they want on device," he says. "I think they're trying to develop the capability first and foremost and then trying to figure out efficiency later."

The path to developing more advanced AI capabilities hasn't exactly been smooth, with key features painfully delayed and next-gen Siri pushed further and further back-with the upgrade now not expected until spring 2026. Recent Bloomberg reporting suggests that Apple leadership is weighing a move to OpenAI or Anthropic technology to help finally deliver its AI promises for a new version of Siri, with tests of these outside models on Apple's cloud infrastructure. The question is: Will Lisa Jackson, Apple's vice president of environment, policy, and social initiatives, be at that table when these decisions are made?

Even if she is, there is no getting away from the electricity needed to manufacture the advanced semiconductor chips powering on-device Apple Intelligence (like the A18 and A18 Pro chips) and Apple's own AI servers ( reportedly Apple's M4 chips as of this year). "Taiwan doesn't have such a rapid deployment of renewable energy, and South Korea, another place that fabricates a lot of chips, doesn't have a lot of it at all," says Lee.

In March, TSMC-the Taiwan-based semiconductor chipmaker which powers flagship iPhones-put out a 2024 report, including a section on ESG: There was a 19 percent increase in greenhouse gas emissions per product unit versus its target of a 10 percent decrease. There was also a 14 percent increase in water use and, drumroll please, only around 14 percent of its used energy came from renewables.

The TSMC press team, when asked for comment, pointed to TSMC accelerating its renewable energy timeline by 10 years in 2023, a 20-year joint procurement agreement for 20,000 gigawatt-hours of renewable energy, and its partnership with Apple on the Restore Fund for carbon-removal projects.

But Greenpeace's Lena Chang wants to see TSMC investing more into Taiwan's wind, solar, and geothermal energy industries, following Google's lead there. "The majority of TSMC's renewable energy is purchased," she says. "They can take the initiative to invest more. From a passive consumer to a proactive prosumer, that's what we are trying to call out TSMC to do more."

An iPhone Afterlife

Of course one of the trickiest threads to solve in all of this is the incompatibility of building AI-capable chips for iPhones which are used on average for just two and a half years before a customer upgrades to a new device.

"Unlike data centers, which buy hardware and then deploy them for very long lifetimes and get very high utilization, you don't get that in the consumer electronics side," says Lee. "Apple would like us to refresh our hardware every two years or so. So that's the difficulty. You have the Scope 3 emissions numbers [for the supply chain], then very high refresh rates and relatively poor utilization."

He believes that after years of flattening in performance and a commoditization of general-purpose CPUs, we otherwise would have seen a slowing down in overall hardware refresh rates: "I think AI is probably restarting the cadence of refresh."

If we continue to get an iPhone launch annually, if Cupertino continues to pack in advanced Apple silicon into every premium device and upgrade cycles remain at 2.5 years, then attention to the afterlife of iPhones becomes more pressing. Globally, Apple makes up just over half the entire secondary smartphone market and while around one in three owners in the US resell their smartphone, that figure drops to around one in five in Europe.

That's not helped by the fact that the amount Apple currently offers to recover traded-in iPhones is much less than the competition, says Counterpoint's Jan Stryjak. Elsewhere, in some countries, there's simply too many different prices and gradings on offer to make sense for the average phone owner: "This is something the industry needs to rectify, to help with giving those older iPhones that second life."

The Next Five Years

Apple appears not to be blind to the challenges it is facing. In April, Lisa Jackson said this in a press release on Apple's environmental progress: "As we get closer to 2030, the work gets even harder-and we're meeting the challenge with innovation, collaboration, and urgency."

Apple is ]the third biggest company in the world by market cap. As its AI agent strategy becomes clearer, environment charities and sustainability experts see a number of ways that it could strengthen its work toward its 2030 climate goals to counteract new innovation.

Then again, the first and second biggest companies in the world? Nvidia and Microsoft, two of the companies at the forefront of the shift toward AI. "We will keep campaigning on the supply chain decarbonization, especially now there seems to be no stopping in AI technology," says Greenpeace's Lena Chang. "It is really important that we keep the balance between AI and sustainable development."

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Apple's AI Ambitions Leave Big Questions Over Its Climate Goals

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Halfway to its 2030 net-zero goal, Apple faces slow and hold-out suppliers, a tariffs scramble, and an AI race that could profoundly impact eco-friendly ambitions.

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Chapman: AI in the Classroom, Resources/Ideas Prepared by CETL

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Chapman: AI in the Classroom, Resources/Ideas Prepared by CETL

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Artificial Intelligence in the Classroom

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Artificial Intelligence in the Classroom

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AI Skepticism in Higher Education: Cultivating Critical Engagement – Teaching with AI Technology

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Kristi Girdharry’s reflection, “Teaching With AI: A Journey Through Grief,” traces how faculty move from denial and anger to bargaining, depression, and ultimately acceptance as generative AI unsettles long‑held practices and identities in writing and teaching. In moments of low psychological safety and limited digital dexterity, that emotional arc can magnify loss of control and lead educators to overstate AI’s downsides, fearing homogenization, assessment collapse, and value erosion, until structures for dialogue and practice create room for balanced, critical engagement.

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What Is the Environmental Cost of Generative AI?

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What Is the Environmental Cost of Generative AI?

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What Is the Environmental Cost of Generative AI? - Impakter

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Generative AI has a high environmental cost. Here’s how much energy and water AI uses, and what can be done to make it more sustainable.

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University students offload critical thinking, other hard work to AI

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University students offload critical thinking, other hard work to AI

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University students offload critical thinking, other hard work to AI

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Two studies indicate students are using chatbots to undermine what they learn

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Alchemy: Bridging Global Insights and Campus Action

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Alchemy: Bridging Global Insights and Campus Action

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Leveraging Generative AI in Educator Preparation Programs: Preparing Inclusive and Equitable Educators

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Leveraging Generative AI in Educator Preparation Programs: Preparing Inclusive and Equitable Educators

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Leveraging Generative AI in Educator Preparation Programs: Preparing Inclusive and Equitable Educators

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This chapter offers actionable guidance on the ethical use of generative AI (genAI) in Educator Preparation Programs (EPPs). GenAI literacy is critical for both students and educators. Thus this chapter discusses the skills and dispositions needed to effectively and ethically use genAI in the classr…

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Unlocking Educational Potential with Ollama

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Explore the transformative power of Ollama in education technology, paving the way for personalized and engaging learning experiences.

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L.O.V.E.: Ethics of Generative AI in Education

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L.O.V.E.: Ethics of Generative AI in Education

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Not your computer? Use Guest mode to sign in privately. Learn more about using Guest mode

# data[].attributes.headline
Making AI Generative for Higher Education

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Making AI Generative for Higher Education

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https://sr.ithaka.org/publications/making-ai-generative-for-higher-education/

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Alchemy AI in Higher Ed YouTube Playlist

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Alchemy AI in Higher Ed YouTube Playlist

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Share your videos with friends, family, and the world

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AI in ED Custom Bots/GPTs Bulletin Board

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AI in ED Custom Bots/GPTs Bulletin Board

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Fit

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AI in ED Custom Bots/GPTs Bulletin Board.pptx

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Share your videos with friends, family, and the world

# data[].attributes.headline
Guidance and Policy Tracker AI in Education

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Guidance and Policy Tracker AI in Education

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See the latest guidance and policies on AI in education from states in the U.S. and worldwide.

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AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

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AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

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Jan 21, 2025

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Angela Gunder, Joshua Herron, Nicole Weber, Colette Chelf, and Sherry Birdwell

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AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

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Examining how AI influences open collaboration, the creation of OERs, and the alignment of resources and initiatives to the needs and values of the open community.

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AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

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AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

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AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

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This paper explores the intersection of AI literacies and open educational practices (OEP) in fostering an "opened culture" across learning environments and communities. The authors situate AI literacies as a set of interconnected competencies transcending technology use with a propensity for advancing the goals of open pedagogy

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Redefining Academic Integrity in the Age of AI (K-State AI Symposium)

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Redefining Academic Integrity in the Age of AI (K-State AI Symposium)

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K-State AI Symposium

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HANDOUT: Redefining Academic Integrity in the Age of AI

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AI in the Workplace 2025 (Digital Education Council)

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AI in the Workplace 2025 (Digital Education Council)

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Digital Education Council | AI in the Workplace 2025

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Kindly fill in the form below if you would like to get access and download AI in the Workplace 2025.

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Opened Culture | Register for AI Literacies Unlocked

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EdGenerative helps schools and universities navigate the safe and ethical use of generative AI through policy development, training, and implementation.

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Thinking in an AI-Augmented World | Askwith Education Forum

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Thinking in an AI-Augmented World | Askwith Education Forum

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Thinking in an AI-Augmented World | Askwith Education Forum

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Georgetown Artificial Intelligence (AI) Toolkit

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Georgetown Artificial Intelligence (AI) Toolkit

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Impact of AI assistance on student agency

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Impact of AI assistance on student agency

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Empower Your Faculty with AI Literacy - Alchemy

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Equip faculty, instructional designers, and faculty developers with free, self-paced micro-credentials in AI literacy. Built on the LEAD framework, each asynchronous credential takes 90 minutes or less to complete.

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Anthropic settles with authors in first-of-its-kind AI copyright infringement lawsuit

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Anthropic settles with authors in first-of-its-kind AI copyright infringement lawsuit

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Anthropic settles with authors in first-of-its-kind AI copyright infringement lawsuit

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A U.S. district court is scheduled to consider whether to approve the settlement next week, in a case that marked the first substantive decision on how fair use applies to generative AI systems.

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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

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AI in Assessment: Enhancing Integrity, Equity, and Innovation in Digital Learning

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AI in Assessment: Enhancing Integrity, Equity, and Innovation in Digital Learning

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done.

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Hong & Shin (2025) - Effects of three levels of AI integration on second language academic writing: Evaluating restricted, guided, and free use of ChatGPT

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Hong & Shin (2025) - Effects of three levels of AI integration on second language academic writing: Evaluating restricted, guided, and free use of ChatGPT

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Eventbrite brings people together through live experiences. Discover events that match your passions, or create your own with online ticketing tools.

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Initial AI Discussion (from UVA teaching hub)

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Initial AI Discussion (from UVA teaching hub)

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Onboarding Activity - Initial AI Discussion 07-14-2025 (1)

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These Students Use AI a Lot — but Not to Cheat

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For some students, the technology can effectively replace collaborative learning with classmates and office hours with professors.

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Leading Through Disruption: Higher Education Executives Assess AI’s Impacts on Teaching and Learning

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Leading Through Disruption: Higher Education Executives Assess AI’s Impacts on Teaching and Learning

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https://dgmg81phhvh63.cloudfront.net/content/user-photos/AACU_AI_Report_2025.pdf

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These College Professors Will Not Bow Down to A.I.

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These College Professors Will Not Bow Down to A.I.

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Researchers Reveal Startling Psychology Behind How AI Biases Affect Humans

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Researchers Reveal Startling Psychology Behind How AI Biases Affect Humans

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Researchers Reveal Startling Psychology Behind How AI Biases Affect Humans

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New University College London (UCL) research has discovered how biased AI systems can influence biased beliefs in humans.

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I Teaching Creative Writing. This is what AI is Doing to My Students.

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I Teaching Creative Writing. This is what AI is Doing to My Students.

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AI can help personalize learning in education industry, says Khan Academy CEO (2m41s)

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AI can help personalize learning in education industry, says Khan Academy CEO (2m41s)

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AI can help personalize learning in education industry, says Khan Academy CEO

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CMU: 2023 - Generative AI, and Adapting to Generative AI in the Classroom

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CMU: 2023 - Generative AI, and Adapting to Generative AI in the Classroom

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Spotlight on Innovative CMU Teaching - Eberly Center - Carnegie Mellon University

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AI in Academic Tasks: From Policy to Practice

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AI in Academic Tasks: From Policy to Practice

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How (not) to use the AI Assessment Scale

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How (not) to use the AI Assessment Scale

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How (not) to use the AI Assessment Scale | Journal of Applied Learning and Teaching

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The rapid uptake of generative AI (GenAI) has exposed weaknesses in assessment design and policy. The AI Assessment Scale (AIAS) offers a practical way to align permitted AI use with intended learning outcomes. However, as the scale has grown, we have seen implementations that we feel could be improved: unenforceable ‘No AI’ labels, assigning labels to existing assessments without changing the assessments themselves, equity blind spots, and control-first policies that encourage ‘performance theatre’.
This commentary outlines our recommendations for AIAS implementations that replace detection with design: selecting levels by outcome and conditions, requiring light process evidence where suitable, adjusting criteria to assess judgement and voice, sequencing evidence across time, and planning for equitable access. We highlight sector-specific considerations for implementing and adapting the AIAS for K-12, higher education, Technical and Vocational Education and Training (TVET), English as a Foreign Language (EFL), and English for Academic Purposes (EAP).

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Academics must be open to changing their minds on acceptable AI use

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Academics must be open to changing their minds on acceptable AI use

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Academics must be open to changing their minds on acceptable AI use

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Honest and open-ended conversations over how AI can be productively used in the learning journey are needed, not ChatGPT bans, says Ava Doherty

# data[].attributes.headline
Frameworks and Activities for Fostering AI Literacy (UVA)

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Frameworks and Activities for Fostering AI Literacy (UVA)

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Frameworks and Activities for Fostering AI Literacy — UVA Teaching Hub

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This collection features frameworks for understanding AI literacy—including one framework developed here at UVA—as well as classroom activities that support the development of AI literacy in both students and instructors.

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Our new study found AI is wreaking havoc on uni assessments. Here’s how we should respond

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Our new study found AI is wreaking havoc on uni assessments. Here’s how we should respond

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Our new study found AI is wreaking havoc on uni assessments. Here’s how we should respond

# data[].attributes.attachment_link.description
New research argues the problem of AI and assessment is way more difficult than media debates have been making out.

# data[].attributes.headline
To What Extent Have Your Assessment Practices Changed in Light of AI? (Alchemy)

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To What Extent Have Your Assessment Practices Changed in Light of AI? (Alchemy)

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GenAI in Instructional Design: Survey Findings on ID Use, Challenges, and Needs

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GenAI in Instructional Design: Survey Findings on ID Use, Challenges, and Needs

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https://www.youtube.com/watch?v=XhbUfPKSk2s

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Is AI Finally a Way to Reduce Higher Ed Costs?

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Is AI Finally a Way to Reduce Higher Ed Costs?

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Inside Higher Ed | Higher Education News, Events and Jobs

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Is AI finally a way to reduce higher ed costs? (opinion)

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AI could free up faculty time to focus on the teaching and relationship-building that matter most, José Antonio Bowen and C. Edward Watson write.

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Ep. 7 – AI, Cheating, and Trusting Students to be Human (Jim Lang)

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Ep. 7 – AI, Cheating, and Trusting Students to be Human (Jim Lang)

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Marketing Communications: Web | University of Notre Dame

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Ep. 7 – AI, Cheating, and Trusting Students to be Human

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Tricia Bertram Gallant, coauthor of The Opposite of Cheating: Teaching for Integrity in the Age of AI, joins us to discuss questions about academic dish…

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TEQSA (2023) - Assessment reform for the age of artificial intelligence

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TEQSA (2023) - Assessment reform for the age of artificial intelligence

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artificial intelligence

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I Spent 24 Hours in the Woods with Talking AI Chatbots

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I Spent 24 Hours in the Woods with Talking AI Chatbots

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I Spent 24 Hours in the Woods With Talking AI Chatbots | WSJ

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To continue, please click the box below to let us know you’re not a robot.

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AI-Powered Impostors Are Getting Hired. Here’s How

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Fake workers are infiltrating U.S. companies. Learn how synthetic employees are getting hired, and what steps employers must take to stop AI-powered hiring fraud.

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Will AI Agents Steal Your Job or Help You Do It Better?

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Will AI Agents Steal Your Job or Help You Do It Better?

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AI agents are changing the future of work. Hubspot, Adobe, Salesforce, Airtop and others are releasing more every day.

getty

Updated Sept 11 with a comment from Salesforce.
Generative AI is taking the next big leap from machines that rip, mix and burn words, images and videos to agents that can create and follow complex multistep project plans to complete significant projects and automate major portions of jobs. And that raises a big, big question for us old-fashioned meatbag humans: are we just an outdated model now?

After all, Salesforce CEO Marc Benioff just told the world he cut 4,000 customer service roles because AI agents have reduced support time and costs.

"I need less heads," he told the podcast The Logan Bartlett Show.

Global AI spend will hit $632 billion by 2028, IDC says, and the IMF thinks AI will affect almost 40% of all jobs globally. So this is a big deal for everyone.

The new AI agents will write blog posts, answer customer support inquiries, research companies, optimize websites, provide data-driven insights, manage clinical trials and much more. For the companies launching them, these agents aren't just copilots: they're becoming semiautonomous digital teammates embedded in all the work a company does.

Here are just a few of the dozens of AI agent pitches I've received in recent days:

HubSpot launched 15 new agents plus an agent marketplace.

Adobe announced six agents and an "agent composer" for companies to control them.

Airtop released a conversational interface to build your own agents just by typing.

Unily has launched an "Agent Orchestrator" to manage AI agents.

SK Telecom in Korea just announced a partnership to develop AI agents.

Grove AI has launched an agent for clinical trials.

From a certain perspective, these AI agents could be seen as a new kind of robot Terminator, one that kills jobs instead of people. Industry analyst Jeremiah Owyang, for instance, has a theory that we will soon see an "autonomous organization" created by AI or AI agents that doesn't need any people at all.

"It will offer services that people will purchase, it will generate revenue on its own, it will operate without any humans at the helm," Owyang suggests.

He's reading the tea leaves well. That's likely to be some Silicon Valley techno-nerd's paradise of the future, and it's probably being built right now. But it's not likely to be worker friendly or pro-social. At least, not without some safeguards.

For others, the whole AI agents movement is just the next step in the evolution of work.

"Like the Industrial Revolution didn't eliminate farming, AI won't eliminate entire jobs," Airtop CEO Amir Ashkenazi told me last week, noting that before the industrial revolution, 80% of humans worked in farming, while now it's fewer than 2%. "Every revolution has transformed the job market."

Airtop's contribution to the revolution is "democratizing automation" via a conversational builder that helps you build your own agents, perhaps to reclaim some of your power, as opposed to the ones your company has bought to automate key parts of your job.

Ashkenazi, for example, uses his own platform to automatically fetch transcripts for all his meetings, forward them to ChatGPT assistant, get back a summary and list of action items and auto-draft a follow-up email for all participants. All he has to do is check the email, perhaps adjust a few things and hit send.

Your role as a human, then, is orchestrator, manager, decider.

"AI is turning everyone into a manager of agents," he told me after a dinner in San Francisco. "In the future, careers will be defined not by how many tasks you can do yourself, but by how effectively you orchestrate a fleet of AI agents around you."

HubSpot seems to take a similar tack, although it's selling AI agents for companies, not specifically people. I chatted with the company's head of product, Karen Ng, at HubSpot's product conference recently.

"We believe in hybrid teams," she told me. "I don't think you can replace human creativity and taste."

The company's CTO, Dharmesh Shah, who released Agent.ai, the "#1 Professional Network for AI Agents" last year, says this is the decade of agents. (I accidentally typed in Agents.ai when researching this article, only to find my "new digital assistant," the interestingly named "B*tch Bot.")

Shah sees agents as force multipliers: making each human better, stronger, faster, smarter.

"The goal is to build with the machine," he said last week in San Francisco. "To help take you ... to the power of AI."

His advice, then, is to list all the things you have to do and accomplish, and try them with AI. Not all will work yet, he says, emphasizing the yet. Shah referenced a study ( likely this one) that demonstrates that AI improves human performance: elevating an individual with AI over the performance of a team of humans.

But a team of humans with AI does the best of all. "They found that an individual using AI outperformed in terms of coming up with better solutions to business problems than a team with no AI," Shah says. "So the individual does become that super contributor with AI. What they also found, and this is less surprising, that teams with AI produce the best results and most creative results to business problems."

Which is aligned with the now-familiar saying: AI won't take your job, but someone using AI will.

The human part is critical, and I can see that from what Salesforce has become, minus the humans, having recently received an email from "Agentforce Sales Agent" which wants to help me help Forbes streamline processes and grow. I didn't bother explaining that "I just write here"; I just unsubscribed.

I also repeatedly emailed Salesforce PR about CEO Benioff's agent and workforce comments, to no response. But I finally received a comment from Salesforce after this story was published.

A company representative said that after launching help services using agents, Salesforce saw reduced support cases, eliminating the need to backfill support engineers. Hundreds of employees have been redeployed to professional services, sales and customer success, the representative said.

As HubSpot's Ng freely admitted, AI can make huge mistakes. In the large language model age of probabilistic answers, not deterministic responses, AI agents will be both amazing and awful, on occasion, and humans need to be in the loop.

There's a lot of good available from AI and the developing AI agents. Automating work that is tedious and dull is a good thing, as long as we can repurpose people to more human tasks.

And it can level playing fields.

"One bright spot is how AI lowers the barrier to entrepreneurship," says Ashkenazi. "People can develop software faster than ever, and with platforms like Airtop, they can launch custom marketing agents to reach their audience. Unicorns with fewer than 10 employees will exist very soon - AI makes it possible."

Of course, as I told him, everyone loves equalization when it means taking them from lower on the food chain to higher. Few love it as much when leveling the playing field means bumping them down a few notches.

The reality, however, is this: AI is here. AI agents are here. They are only getting better, smarter, faster and more capable.

Perhaps now, as ever, the mandate is simple: adapt or die.

In which case your job as a human is to find the best AI agents and customize them to your needs and goals. And to find ways to make everything you do better as a result.

Brave new world?

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Will AI Agents Steal Your Job Or Help You Do It Better?

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Almost every company is releasing AI agents for automating just about everything. So what is the future of human workers?

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College Students Have Already Changed Forever

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College Students Have Already Changed Forever

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College Students Have Already Changed Forever

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Members of the class of 2026 have had access to AI since they were freshmen. Almost all of them are using it to do their work.

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Student Generative AI Survey 2025 - HEPI

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On 26 February, HEPI and Kortext will publish the Student Generative AI Survey 2025 by Josh Freeman, with a Foreword by Professor Janice Kay CBE. Based on a survey of 1,041 students conducted by Savanta, the report shows an unprecedented increase in the use of generative AI tools among undergraduate students from the rates recorded […]

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The Hidden Cost of AI Energy Consumption

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The Hidden Cost of AI Energy Consumption

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The Hidden Cost of AI Energy Consumption

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AI energy consumption will deplete our natural resources if leaders don’t act now, warns Wharton’s Cornelia Walther.

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Will the Humanities Survive Artificial Intelligence?

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Will the Humanities Survive Artificial Intelligence?

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Will the Humanities Survive Artificial Intelligence?

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Maybe not as we’ve known them. But, in the ruins of the old curriculum, something vital is stirring.

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Cynthia Alby - ChatGPT: Understanding the new landscape and short-term solutions 

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Cynthia Alby - ChatGPT: Understanding the new landscape and short-term solutions 

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ChatGPT: Understanding the new landscape and short-term solutions

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Empowered Educator – Professional Development for Faculty

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Explore teaching strategies and trends in higher education with Empowered Educator—professional development for faculty, from live events to recorded webinars.

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Research Guides: Artificial Intelligence (AI) in Higher Ed: What is AI?

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This AI in Higher Education LibGuide brings together a curated collection of research, articles, whitepapers, videos, webinars, and expert interviews that delve into the numerous ways AI is impacting higher education.

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When a Google engineer recently declared Google's AI chatbot a person, pandemonium ensued. The chatbot, LaMDA, is a large language model (LLM) that is designed to predict the likely next words to whatever lines of text it is given. Since many conversations are somewhat predictable, these systems can infer how to keep a conversation going productively. LaMDA did this so impressively that the engineer, Blake Lemoine, began to wonder about whether there was a ghost in the machine.

Reactions to Lemoine's story spanned the gamut: some people scoffed at the mere idea that a machine could ever be a person. Others suggested that this LLM isn't a person, but the next perhaps might be. Still others pointed out that deceiving humans isn't very challenging; we see saints in toast, after all.

But the diversity of responses highlights a deeper problem: as these LLMs become more common and powerful, there seems to be less and less agreement over how we should understand them. These systems have bested many "common sense" linguistic reasoning benchmarks over the years, many which promised to be conquerable only by a machine that "is thinking in the full-bodied sense we usually reserve for people." Yet these systems rarely seem to have the common sense promised when they defeat the test and are usually still prone to blatant nonsense, non sequiturs and dangerous advice. This leads to a troubling question: how can these systems be so smart, yet also seem so limited?

The underlying problem isn't the AI. The problem is the limited nature of language. Once we abandon old assumptions about the connection between thought and language, it is clear that these systems are doomed to a shallow understanding that will never approximate the full-bodied thinking we see in humans. In short, despite being among the most impressive AI systems on the planet, these AI systems will never be much like us.

Saying It All

A dominant theme for much of the 19 th and 20 th century in philosophy and science was that knowledge just is linguistic - that knowing something simply means thinking the right sentence and grasping how it connects to other sentences in a big web of all the true claims we know. The ideal form of language, by this logic, would be a purely formal, logical-mathematical one composed of arbitrary symbols connected by strict rules of inference, but natural language could serve as well if you took the extra effort to clear up ambiguities and imprecisions. As Wittgenstein put it, "The totality of true propositions is the whole of natural science." This position was so established in the 20 th century that psychological findings of cognitive maps and mental images were controversial, with many arguing that, despite appearances, these must be linguistic at base.

This view is still assumed by some overeducated, intellectual types: everything which can be known can be contained in an encyclopedia, so just reading everything might give us a comprehensive knowledge of everything. It also motivated a lot of the early work in Symbolic AI, where symbol manipulation - arbitrary symbols being bound together in different ways according to logical rules - was the default paradigm. For these researchers, an AI's knowledge consisted of a massive database of true sentences logically connected with one another by hand, and an AI system counted as intelligent if it spit out the right sentence at the right time - that is, if it manipulated symbols in the appropriate way. This notion is what underlies the Turing test: if a machine says everything it's supposed to say, that means it knows what it's talking about, since knowing the right sentences and when to deploy them exhausts knowledge.

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But this was subject to a withering critique which has dogged it ever since: just because a machine can talk about anything, that doesn't mean it understands what it is talking about. This is because language doesn't exhaust knowledge; on the contrary, it is only a highly specific, and deeply limited, kind of knowledge representation. All language - whether a programming language, a symbolic logic or a spoken language - turns on a specific type of representational schema; it excels at expressing discrete objects and properties and the relationships between them at an extremely high level of abstraction. But there is a massive difference between reading a musical score and listening to a recording of the music, and a further difference from having the skill to play it.

All representational schemas involve a compression of information about something, but what gets left in and left out in the compression varies. The representational schema of language struggles with more concrete information, such as describing irregular shapes, the motion of objects, the functioning of a complex mechanism or the nuanced brushwork of a painting - much less the finicky, context-specific movements needed for surfing a wave. But there are nonlinguistic representational schemes which can express this information in an accessible way: iconic knowledge, which involves things like images, recordings, graphs and maps; and the distributed knowledge found in trained neural networks - what we often call know-how and muscle memory. Each scheme expresses some information easily even while finding other information hard - or even impossible - to represent: what does "Either Picasso or Twombly" look like?

The Limits Of Language

One way of grasping what is distinctive about the linguistic representational schema - and how it is limited - is recognizing how littleinformation it passes along on its own. Language is a very low-bandwidth method for transmitting information: isolated words or sentences, shorn of context, convey little. Moreover, because of the sheer number of homonyms and pronouns, many sentences are deeply ambiguous: does " the box was in the pen" refer to an ink pen or a playpen? As Chomsky and his acolytes have pointed out for decades, language is just not a clear and unambiguous vehicle for clear communication.

But humans don't need a perfect vehicle for communication because we share a nonlinguistic understanding. Our understanding of a sentence often depends on our deeper understanding of the contexts in which this kind of sentence shows up, allowing us to infer what it is trying to say. This is obvious in conversation, since we are often talking about something directly in front of us, such as a football game, or communicating about some clear objective given the social roles at play in a situation, such as ordering food from a waiter. But the same holds in reading passages - a lesson which not only undermines common-sense language tests in AI but also a popular method of teaching context-free reading comprehension skills to children. This method focuses on using generalized reading comprehension strategies to understand a text - but research suggests that the amount of background knowledge a child has on the topic is actually the key factor for comprehension. Understanding a sentence or passage depends on an underlying grasp of what the topic is about.

The inherently contextual nature of words and sentences is at the heart of how LLMs work. Neural nets in general represent knowledge as , the skillful ability to grasp highly context-sensitive patterns and find regularities - both concrete and abstract - necessary for handling inputs in nuanced ways that are narrowly tailored to their task. In LLMs, this involves the system discerning patterns at multiple levels in existing texts, seeing both how individual words are connected in the passage but also how the sentences all hang together within the larger passage which frames them. The result is that its grasp of language is ineliminably contextual; every word is understood not on its dictionary meaning but in terms of the role it plays in a diverse collection of sentences. Since many words - think "carburetor," "menu," "debugging" or "electron" - are almost exclusively used in specific fields, even an isolated sentence with one of these words carries its context on its sleeve.

In short, LLMs are trained to pick up on the background knowledge for each sentence, looking to the surrounding words and sentences to piece together what is going on. This allows them to take an infinite possibility of different sentences or phrases as input and come up with plausible (though hardly flawless) ways to continue the conversation or fill in the rest of the passage. A system trained on passages written by humans, often conversing with each other, should come up with the general understanding necessary for compelling conversation.

Shallow Understanding

While some balk at using the term "understanding" in this context or calling LLMs "intelligent," it isn't clear what semantic gatekeeping is buying anyone these days. But critics are right to accuse these systems of being engaged in a kind of mimicry. This is because LLMs' understanding of language, while impressive, is shallow. This kind of shallow understanding is familiar; classrooms are filled with jargon-spouting students who don't know what they're talking about - effectively engaged in a mimicry of their professors or the texts they are reading. This is just part of life; we often don't know how little we know, especially when it comes to knowledge acquired from language.

LLMs have acquired this kind of shallow understanding about everything. A system like GPT-3 is trained by masking the future words in a sentence or passage and forcing the machine to guess what word is most likely, then being corrected for bad guesses. The system eventually gets proficient at guessing the most likely words, making them an effective predictive system.

This brings with it some genuine understanding: for any question or puzzle, there are usually only a few right answers but an infinite number of wrong answers. This forces the system to learn language-specific skills, such as explaining a joke, solving a word problem or figuring out a logic puzzle, in order to regularly predict the right answer on these types of questions. These skills, and the connected knowledge, allow the machine to explain how something complicated works, simplify difficult concepts, rephrase and retell stories, along with a host of other language-dependent abilities. Instead of a massive database of sentences linked by logical rules, as Symbolic AI assumed, the knowledge is represented as context-sensitive know-how for coming up with a plausible sentence given the prior line.

But the ability to explain a concept linguistically is different from the ability to use it practically. The system can explain how to perform long division without being able to perform it or explain what words are offensive and should not be said while then blithely going on to say them. The contextual knowledge is embedded in one form - the capacity to rattle off linguistic knowledge - but is not embedded in another form - as skillful know-how for how to do things like being empathetic or handling a difficult issue sensitively.

The latter kind of know-how is essential to language users, but that doesn't make them linguistic skills - the linguistic component is incidental, not the main thing. This applies to many concepts, even those learned from lectures and books: while science classes do have a lecture component, students are graded primarily based on their lab work. Outside the humanities especially, being able to talk about something is often less useful or important than the nitty-gritty skills needed to get things to work right.

Once we scratch beneath the surface, it is easier to see how limited these systems really are: they have the attention span and memory of roughly a paragraph. This can easily be missed if we engage in a conversation because we tend to focus on just the last comment or two and focus only on our next response.

But the know-how for more complex conversations - active listening, recall and revisiting prior comments, sticking to a topic to make a specific point while fending off distractors, and so on - all require more attention and memory than the system possesses. This reduces even further what kind of understanding is available to them: it is easy to trick them simply by being inconsistent every few minutes, changing languages or gaslighting the system. If it is too many steps back, the system will just start over, accepting your new views as consistent with older comments, switching languages with you or acknowledging it believes whatever you said. The understanding necessary for developing a coherent view of the world is far beyond their ken.

Beyond Language

Abandoning the view that all knowledge is linguistic permits us to realize how much of our knowledge is nonlinguistic. While books contain a lot of information we can decompress and use, so do many other objects: IKEA instructions don't even bother writing out instructions alongside its drawings; AI researchers often look at the diagrams in a paper first, grasp the network architecture and only then glance through the text; visitors can navigate NYC by following the red or green lines on a map.

This goes beyond simple icons, graphs and maps. Humans learn a lot directly from exploring the world, which shows us how objects and people can and cannot behave. The structures of artifacts and the human environment convey a lot of information intuitively: doorknobs are at hand height, hammers have soft grips and so on. Nonlinguistic mental simulation, in animals and humans, is common and useful for planning out scenarios and can be used to craft, or reverse-engineer, artifacts. Similarly, social customs and rituals can convey all kinds of skills to the next generation through imitation, extending from preparing foods and medicines to maintaining the peace at times of tension. Much of our cultural knowledge is iconic or in the form of precise movements passed on from skilled practitioner to apprentice. These nuanced patterns of information are hard to express and convey in language but are still accessible to others. This is also the precise kind of context-sensitive information that neural networks excel at picking up and perfecting.

Language is important because it can convey a lot of information in a small format and, especially after the creation of the printing press and the internet, can involve reproducing and making it available widely. But compressing information in language isn't cost-free: it takes a lot of effort to decode a dense passage. Humanities classes may require a lot of reading out of class, but a good chunk of class time is still spent going over difficult passages. Building a deep understanding is time-consuming and exhaustive, however the information is provided.

This explains why a machine trained on language can know so much and yet so little. It is acquiring a small part of human knowledge through a tiny bottleneck. But that small part of human knowledge can be about anything, whether it be love or astrophysics. It is thus a bit akin to a mirror: it gives the illusion of depth and can reflect almost anything, but it is only a centimeter thick. If we try to explore its depths, we bump our heads.

Exorcising The Ghost

This doesn't make these machines stupid, but it also suggests there are intrinsic limits concerning how smart they can be. A system trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe. This is just the wrong kind of knowledge for developing awareness or being a person. But they will undoubtedly seem to approximate it if we stick to the surface. And, in many cases, the surface is enough; few of us really apply the Turing test to other people, aggressively querying the depth of their understanding and forcing them to do multidigit multiplication problems. Most talk is small talk.

But we should not confuse the shallow understanding LLMs possess for the deep understanding humans acquire from watching the spectacle of the world, exploring it, experimenting in it and interacting with culture and other people. Language may be a helpful component which extends our understanding of the world, but language doesn't exhaust intelligence, as is evident from many species, such as corvids, octopi and primates.

Rather, the deep nonlinguistic understanding is the ground that makes language useful; it's because we possess a deep understanding of the world that we can quickly understand what other people are talking about. This broader, context-sensitive kind of learning and know-how is the more basic and ancient kind of knowledge, one which underlies the emergence of sentience in embodied critters and makes it possible to survive and flourish. It is also the more essential task that AI researchers are focusing on when searching for common sense in AI, rather than this linguistic stuff. LLMs have no stable body or abiding world to be sentient of-so their knowledge begins and ends with more words and their common-sense is always skin-deep. The goal is for AI systems to focus on the world being talked about, not the words themselves - but LLMs don't grasp the distinction. There is no way to approximate this deep understanding solely through language; it's just the wrong kind of thing. Dealing with LLMs at any length makes apparent just how little can be known from language alone.

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An artificial intelligence system trained on words and sentences alone will never approximate human understanding.

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When a Google engineer recently declared Google's AI chatbot a person, pandemonium ensued. The chatbot, LaMDA, is a large language model (LLM) that is designed to predict the likely next words to whatever lines of text it is given. Since many conversations are somewhat predictable, these systems can infer how to keep a conversation going productively. LaMDA did this so impressively that the engineer, Blake Lemoine, began to wonder about whether there was a ghost in the machine.

Reactions to Lemoine's story spanned the gamut: some people scoffed at the mere idea that a machine could ever be a person. Others suggested that this LLM isn't a person, but the next perhaps might be. Still others pointed out that deceiving humans isn't very challenging; we see saints in toast, after all.

But the diversity of responses highlights a deeper problem: as these LLMs become more common and powerful, there seems to be less and less agreement over how we should understand them. These systems have bested many "common sense" linguistic reasoning benchmarks over the years, many which promised to be conquerable only by a machine that "is thinking in the full-bodied sense we usually reserve for people." Yet these systems rarely seem to have the common sense promised when they defeat the test and are usually still prone to blatant nonsense, non sequiturs and dangerous advice. This leads to a troubling question: how can these systems be so smart, yet also seem so limited?

The underlying problem isn't the AI. The problem is the limited nature of language. Once we abandon old assumptions about the connection between thought and language, it is clear that these systems are doomed to a shallow understanding that will never approximate the full-bodied thinking we see in humans. In short, despite being among the most impressive AI systems on the planet, these AI systems will never be much like us.

Saying It All

A dominant theme for much of the 19 th and 20 th century in philosophy and science was that knowledge just is linguistic - that knowing something simply means thinking the right sentence and grasping how it connects to other sentences in a big web of all the true claims we know. The ideal form of language, by this logic, would be a purely formal, logical-mathematical one composed of arbitrary symbols connected by strict rules of inference, but natural language could serve as well if you took the extra effort to clear up ambiguities and imprecisions. As Wittgenstein put it, "The totality of true propositions is the whole of natural science." This position was so established in the 20 th century that psychological findings of cognitive maps and mental images were controversial, with many arguing that, despite appearances, these must be linguistic at base.

This view is still assumed by some overeducated, intellectual types: everything which can be known can be contained in an encyclopedia, so just reading everything might give us a comprehensive knowledge of everything. It also motivated a lot of the early work in Symbolic AI, where symbol manipulation - arbitrary symbols being bound together in different ways according to logical rules - was the default paradigm. For these researchers, an AI's knowledge consisted of a massive database of true sentences logically connected with one another by hand, and an AI system counted as intelligent if it spit out the right sentence at the right time - that is, if it manipulated symbols in the appropriate way. This notion is what underlies the Turing test: if a machine says everything it's supposed to say, that means it knows what it's talking about, since knowing the right sentences and when to deploy them exhausts knowledge.

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Deep Learning Alone Isn't Getting Us To Human-Like AI What AI Can Tell Us About Intelligence The Model Is The Message
But this was subject to a withering critique which has dogged it ever since: just because a machine can talk about anything, that doesn't mean it understands what it is talking about. This is because language doesn't exhaust knowledge; on the contrary, it is only a highly specific, and deeply limited, kind of knowledge representation. All language - whether a programming language, a symbolic logic or a spoken language - turns on a specific type of representational schema; it excels at expressing discrete objects and properties and the relationships between them at an extremely high level of abstraction. But there is a massive difference between reading a musical score and listening to a recording of the music, and a further difference from having the skill to play it.

All representational schemas involve a compression of information about something, but what gets left in and left out in the compression varies. The representational schema of language struggles with more concrete information, such as describing irregular shapes, the motion of objects, the functioning of a complex mechanism or the nuanced brushwork of a painting - much less the finicky, context-specific movements needed for surfing a wave. But there are nonlinguistic representational schemes which can express this information in an accessible way: iconic knowledge, which involves things like images, recordings, graphs and maps; and the distributed knowledge found in trained neural networks - what we often call know-how and muscle memory. Each scheme expresses some information easily even while finding other information hard - or even impossible - to represent: what does "Either Picasso or Twombly" look like?

The Limits Of Language

One way of grasping what is distinctive about the linguistic representational schema - and how it is limited - is recognizing how littleinformation it passes along on its own. Language is a very low-bandwidth method for transmitting information: isolated words or sentences, shorn of context, convey little. Moreover, because of the sheer number of homonyms and pronouns, many sentences are deeply ambiguous: does " the box was in the pen" refer to an ink pen or a playpen? As Chomsky and his acolytes have pointed out for decades, language is just not a clear and unambiguous vehicle for clear communication.

But humans don't need a perfect vehicle for communication because we share a nonlinguistic understanding. Our understanding of a sentence often depends on our deeper understanding of the contexts in which this kind of sentence shows up, allowing us to infer what it is trying to say. This is obvious in conversation, since we are often talking about something directly in front of us, such as a football game, or communicating about some clear objective given the social roles at play in a situation, such as ordering food from a waiter. But the same holds in reading passages - a lesson which not only undermines common-sense language tests in AI but also a popular method of teaching context-free reading comprehension skills to children. This method focuses on using generalized reading comprehension strategies to understand a text - but research suggests that the amount of background knowledge a child has on the topic is actually the key factor for comprehension. Understanding a sentence or passage depends on an underlying grasp of what the topic is about.

The inherently contextual nature of words and sentences is at the heart of how LLMs work. Neural nets in general represent knowledge as , the skillful ability to grasp highly context-sensitive patterns and find regularities - both concrete and abstract - necessary for handling inputs in nuanced ways that are narrowly tailored to their task. In LLMs, this involves the system discerning patterns at multiple levels in existing texts, seeing both how individual words are connected in the passage but also how the sentences all hang together within the larger passage which frames them. The result is that its grasp of language is ineliminably contextual; every word is understood not on its dictionary meaning but in terms of the role it plays in a diverse collection of sentences. Since many words - think "carburetor," "menu," "debugging" or "electron" - are almost exclusively used in specific fields, even an isolated sentence with one of these words carries its context on its sleeve.

In short, LLMs are trained to pick up on the background knowledge for each sentence, looking to the surrounding words and sentences to piece together what is going on. This allows them to take an infinite possibility of different sentences or phrases as input and come up with plausible (though hardly flawless) ways to continue the conversation or fill in the rest of the passage. A system trained on passages written by humans, often conversing with each other, should come up with the general understanding necessary for compelling conversation.

Shallow Understanding

While some balk at using the term "understanding" in this context or calling LLMs "intelligent," it isn't clear what semantic gatekeeping is buying anyone these days. But critics are right to accuse these systems of being engaged in a kind of mimicry. This is because LLMs' understanding of language, while impressive, is shallow. This kind of shallow understanding is familiar; classrooms are filled with jargon-spouting students who don't know what they're talking about - effectively engaged in a mimicry of their professors or the texts they are reading. This is just part of life; we often don't know how little we know, especially when it comes to knowledge acquired from language.

LLMs have acquired this kind of shallow understanding about everything. A system like GPT-3 is trained by masking the future words in a sentence or passage and forcing the machine to guess what word is most likely, then being corrected for bad guesses. The system eventually gets proficient at guessing the most likely words, making them an effective predictive system.

This brings with it some genuine understanding: for any question or puzzle, there are usually only a few right answers but an infinite number of wrong answers. This forces the system to learn language-specific skills, such as explaining a joke, solving a word problem or figuring out a logic puzzle, in order to regularly predict the right answer on these types of questions. These skills, and the connected knowledge, allow the machine to explain how something complicated works, simplify difficult concepts, rephrase and retell stories, along with a host of other language-dependent abilities. Instead of a massive database of sentences linked by logical rules, as Symbolic AI assumed, the knowledge is represented as context-sensitive know-how for coming up with a plausible sentence given the prior line.

But the ability to explain a concept linguistically is different from the ability to use it practically. The system can explain how to perform long division without being able to perform it or explain what words are offensive and should not be said while then blithely going on to say them. The contextual knowledge is embedded in one form - the capacity to rattle off linguistic knowledge - but is not embedded in another form - as skillful know-how for how to do things like being empathetic or handling a difficult issue sensitively.

The latter kind of know-how is essential to language users, but that doesn't make them linguistic skills - the linguistic component is incidental, not the main thing. This applies to many concepts, even those learned from lectures and books: while science classes do have a lecture component, students are graded primarily based on their lab work. Outside the humanities especially, being able to talk about something is often less useful or important than the nitty-gritty skills needed to get things to work right.

Once we scratch beneath the surface, it is easier to see how limited these systems really are: they have the attention span and memory of roughly a paragraph. This can easily be missed if we engage in a conversation because we tend to focus on just the last comment or two and focus only on our next response.

But the know-how for more complex conversations - active listening, recall and revisiting prior comments, sticking to a topic to make a specific point while fending off distractors, and so on - all require more attention and memory than the system possesses. This reduces even further what kind of understanding is available to them: it is easy to trick them simply by being inconsistent every few minutes, changing languages or gaslighting the system. If it is too many steps back, the system will just start over, accepting your new views as consistent with older comments, switching languages with you or acknowledging it believes whatever you said. The understanding necessary for developing a coherent view of the world is far beyond their ken.

Beyond Language

Abandoning the view that all knowledge is linguistic permits us to realize how much of our knowledge is nonlinguistic. While books contain a lot of information we can decompress and use, so do many other objects: IKEA instructions don't even bother writing out instructions alongside its drawings; AI researchers often look at the diagrams in a paper first, grasp the network architecture and only then glance through the text; visitors can navigate NYC by following the red or green lines on a map.

This goes beyond simple icons, graphs and maps. Humans learn a lot directly from exploring the world, which shows us how objects and people can and cannot behave. The structures of artifacts and the human environment convey a lot of information intuitively: doorknobs are at hand height, hammers have soft grips and so on. Nonlinguistic mental simulation, in animals and humans, is common and useful for planning out scenarios and can be used to craft, or reverse-engineer, artifacts. Similarly, social customs and rituals can convey all kinds of skills to the next generation through imitation, extending from preparing foods and medicines to maintaining the peace at times of tension. Much of our cultural knowledge is iconic or in the form of precise movements passed on from skilled practitioner to apprentice. These nuanced patterns of information are hard to express and convey in language but are still accessible to others. This is also the precise kind of context-sensitive information that neural networks excel at picking up and perfecting.

Language is important because it can convey a lot of information in a small format and, especially after the creation of the printing press and the internet, can involve reproducing and making it available widely. But compressing information in language isn't cost-free: it takes a lot of effort to decode a dense passage. Humanities classes may require a lot of reading out of class, but a good chunk of class time is still spent going over difficult passages. Building a deep understanding is time-consuming and exhaustive, however the information is provided.

This explains why a machine trained on language can know so much and yet so little. It is acquiring a small part of human knowledge through a tiny bottleneck. But that small part of human knowledge can be about anything, whether it be love or astrophysics. It is thus a bit akin to a mirror: it gives the illusion of depth and can reflect almost anything, but it is only a centimeter thick. If we try to explore its depths, we bump our heads.

Exorcising The Ghost

This doesn't make these machines stupid, but it also suggests there are intrinsic limits concerning how smart they can be. A system trained on language alone will never approximate human intelligence, even if trained from now until the heat death of the universe. This is just the wrong kind of knowledge for developing awareness or being a person. But they will undoubtedly seem to approximate it if we stick to the surface. And, in many cases, the surface is enough; few of us really apply the Turing test to other people, aggressively querying the depth of their understanding and forcing them to do multidigit multiplication problems. Most talk is small talk.

But we should not confuse the shallow understanding LLMs possess for the deep understanding humans acquire from watching the spectacle of the world, exploring it, experimenting in it and interacting with culture and other people. Language may be a helpful component which extends our understanding of the world, but language doesn't exhaust intelligence, as is evident from many species, such as corvids, octopi and primates.

Rather, the deep nonlinguistic understanding is the ground that makes language useful; it's because we possess a deep understanding of the world that we can quickly understand what other people are talking about. This broader, context-sensitive kind of learning and know-how is the more basic and ancient kind of knowledge, one which underlies the emergence of sentience in embodied critters and makes it possible to survive and flourish. It is also the more essential task that AI researchers are focusing on when searching for common sense in AI, rather than this linguistic stuff. LLMs have no stable body or abiding world to be sentient of-so their knowledge begins and ends with more words and their common-sense is always skin-deep. The goal is for AI systems to focus on the world being talked about, not the words themselves - but LLMs don't grasp the distinction. There is no way to approximate this deep understanding solely through language; it's just the wrong kind of thing. Dealing with LLMs at any length makes apparent just how little can be known from language alone.

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An artificial intelligence system trained on words and sentences alone will never approximate human understanding.

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AI is here – If we fight it, we’ll lose and so will our students!

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AI is here – If we fight it, we’ll lose and so will our students!

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Artificial intelligence (AI) is the simulation of human intelligence by machines. AI research deals with the question of how to create computers that are capable of intelligent behaviour.

In practical terms, AI applications can be deployed in a number of ways, including:

Machine learning: This is a method of teaching computers to learn from data, without being explicitly programmed.

Natural language processing: This involves teaching computers to understand human language and respond in a way that is natural for humans.

Robotics: This involves the use of robots to carry out tasks that would otherwise be difficult or impossible for humans to do.

Predictive analytics: This is a method of using artificial intelligence to make predictions about future events, trends, and behaviours.

We are already using artificial intelligence frequently in our daily lives, often without even realising it. Some common examples include:

Voice assistants such as Amazon Alexa and Apple Siri

Google search and other search engines

Spam filters

Fraud detection

Recommendation engines (e.g. Netflix)

Self-driving cars

It's used in education for lots of good stuff too, but AI has a more contentious side.

Increased Use of AI

Recent studies have highlighted that artificial intelligence is increasingly being used by students to cheat in assignments. They find that students are using AI to generate answers to questions, write essays and even create presentations, and more and more students are turning to it to get ahead.

There are a number of reasons why students are turning to AI to cheat. Firstly, it is relatively easy to access, with many AI tools available online, such as Open AI which we'll discuss later.

Secondly, AI can generate answers and essays that are very close to what a human would write, making it harder for teachers to detect, and the content generated is 'original' in that it has not been created in that way before.

Thirdly, the use of AI is not just limited to the complete authoring of work; students are also using it to improve their grades. For instance, AI can help students to proofread their essays and identify errors that they may have missed. Additionally, AI can also be used to create presentations and lecture content from scratch.

Lastly, and most importantly in my view, students turn to AI because they are increasingly desperate. They frequently have to commute long distances due to housing concerns; they often have to work north of 20 hours per week on top of their studies to make ends meet, and they face a huge assessment burden in college. They're often over-assessed, with huge bunching of deadlines across modules, and often in rigid assessment formats, not flexible enough to cater for the needs of diverse students.

The use of AI in education is a controversial issue. Some argue that it is a valuable tool that can help students to improve their grades. However, others argue that the use of AI is out and out cheating and that it gives an unfair advantage to those who use it. Regardless of the debate, it is clear that the use of AI in education is on the rise. This is likely to continue as AI technology becomes more sophisticated and more accessible.

But often the discourse is heavily skewed towards pointing the finger at students, and using surveillance (and ironically AI) technologies to police their work, rather than examining the role of educators in creating the conditions for academic integrity to flourish. I worry that in the rush to 'clamp down', we will return to more rigid assessment modes like in-person exams, which the evidence shows strongly disadvantage disabled students and other cohorts.

What kind of message does that send? How can we ask for integrity from our students when we are actively using modes that we know are disadvantaging them? Both sides must play their part to create a trusting environment where dishonesty is minimised and fairness and equity are demonstrated.

Another way to promote academic integrity is through education and training. Institutions can offer workshops and seminars on the topic, and make academic integrity a part of new student orientation programs. Faculty can also be encouraged to incorporate academic integrity into their courses.

Perhaps the most important way to promote academic integrity is to highlight the supports available to students who are struggling with their work, introduce more flexible assessment methods, and work across programmes to reduce and space out the assessment load. We should also, in my view, be working to create an environment which places far less weight on competitive grading, and far more emphasis on rewarding growing and learning, which disincentivises cheating.

Ultimately, the promotion of academic integrity is the responsibility of everyone in the higher education community - staff and students alike. By working together, we can create an environment in which academic integrity is valued and upheld.

It's my firm belief that the only way we can address the issue of integrity and AI is through developing a culture of trust, which places equal learning and development opportunities at the heart of decision-making. Technology will always be ahead of us, and the more we try to clamp down and police the issue, the more we will have to turn to disadvantaging modes of assessment and unethical, bias-ridden, surveillance technologies. This is a recipe for further mistrust between our staff and students, and will only breed greater levels of dishonesty in response. If we focus our energies on trying to fight the rise of AI in education in this way, we are destined to lose, and our students will suffer.

But Wait - Should We Be Fighting AI At All?

But there's another reason we shouldn't fight it. In fact, there are many arguments for why higher education institutions should teach their students how to use AI tools to create better work and to build assessments that develop their skills at critically analysing and applying AI outputs.

Firstly, as the world becomes more and more digitised, those who are able to use artificial intelligence tools in any capacity will have an obvious advantage in the job market.

Secondly, artificial intelligence can help professionals in a variety of fields do their jobs more efficiently and effectively. For example, doctors can use artificial intelligence to diagnose diseases more accurately, lawyers can use it to sort through mountains of evidence more quickly, and architects can use it to design buildings that are more energy-efficient. Imagine what creativity we could unleash if more fields knew how to leverage these kinds of technology.

Thirdly, artificial intelligence can help people to automate repetitive tasks. Its already doing that for many of us. For example, if we have to enter the same information into a database over and over again as part of our administration work, we can use an artificial intelligence tool to automate this task. This will not only save time, but it will also reduce the chances of errors being made. Wouldn't you like to have the AI literacy to apply that use?

Overall, there are many reasons why higher education institutions should teach their students how to use artificial intelligence tools to create better work. Artificial intelligence is a growing field with immense potential, and those who are able to use these tools will be well-positioned for success in the future.

Over time, our education system has outlawed the use of many tools we now see as part of our everyday toolkit to be more effective and efficient in our work. Spelling and grammar checkers and calculators for example are tools which were deemed in the past to somehow impinge on academic standards.

When new technologies emerge, our higher education systems tend to mistrust their use before they become normalised. We evolve to accept and rely on them, and when we do, they help us to be wildly more efficient, accurate and effective.

Perhaps we should begin to think now about how we can get ahead of this with AI, and think of the endless creativity and innovation possible if more of our students can master these tools in relation to their fields and their lives. In a world where knowledge is as ubiquitous, easily accessible, and easily adaptable as it is now, perhaps we can think of students work with AI as a collaboration with that collective body of knowledge.

In this world, the line between plagiarism and intelligently and efficiently building on the existing ideas and knowledge of the community becomes blurry.

Take this article for example. To illustrate the point, I've written it in collaboration with AI. About 60-70% of it was written by an Open AI algorithm. The text produced is completely unique, and built on processing the huge body of knowledge the internet holds, using human intelligence models to interpret the commands I gave it. I even used the AI art generator DALL-E to create the original imagery for the piece based on commands. If ye don't believe me watch the video below - I filmed myself doing it.

I first thought through the ideas and structure of the article, then gave the Open AI playground tool commands to write various sections for me, and then joined the dots, edited, and added personal reflection. Is this work mine? Probably. Maybe a collaboration with the world? I'm not sure.

But I think the power of what these tools can do should be harnessed, not rejected. Hugely exciting developments in innovation, equity and inclusion could emerge.

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CEO's Corner - AI is here - If we fight it, we'll lose and so will our students!

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Teaching Students to Write with AI: The SPACE Framework

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Teaching Students to Write with AI: The SPACE Framework

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Teaching Students to Write with AI: The SPACE Framework

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Marc Watkins: AI Will Augment, Not Replace

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Marc Watkins: AI Will Augment, Not Replace

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Join us to explore how AI Teaching Assistant Pro, a free AI-powered personal digital assistant by Contact North | Contact Nord, can simplify and enrich your teaching experience. In this interactive session, you’ll learn how AI Teaching Assistant Pro enables instructors to: Create custom multiple-choice and essay questions with scoring rubrics. Upload course materials to

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