ҵ

There is a large and growing number of resources available on the web for AI in higher education. The list below has been curated for relevance to ҵ and its context of an international liberal arts education. Please send to aiataup.edu any suggestions for this list.

Intros & background

How generative AI works
  • (Andrej Karpathy) ()
Overviews of the generative AI landscape
  • in higher ed
    • (Hanover Research & IHE)
  • in the broader environment
    • (Imagining the Digital Future Center - Elon University)
  • surveys of AI use
Practical overviews for AI in teaching/learning
  • (Anna Mills)
Generative AI tutorials for faculty

(metaLAB (at) Harvard)

Communicating with students about AI & AI use

Syllabus statements & course design
  • Crowd-sourced list of (Lance Eaton)
  • (Lance Cummings)
  • (Oregon State University)
  • (support for different stages/aspects in course/syllabus design)
  • (Carroll College) -- based on key principles: Clarify, Communicate, Uphold, Engage
  • (U Wisconsin)
  • (Inside Higher Ed)
  • – tool for generating clear AI use guidelines according to the nature of an assignment (Ryan Watkins)
  • Engaging With AI Isn't Adopting AI (Marc Watkins) – "normalizing AI disclosure as a means of curbing the uncritical adoption of AI and restoring the trust between professors and students"
  • (Torrey Trust) – and see her detailed (the specificity and the "why is this allowed" may get students' attention in ways that more general language might not)
Discussing AI with students

Academic integrity & AI use
  • AI detection & its issues
    • (Chris Ostro) – "any discussion about AI detection has to be paired with additional training"
    • : "Detection tools for AI-generated text do fail, they are neither accurate nor reliable (all scored below 80% of accuracy and only 5 over 70%)."
    • (Sarah Eaton) – “If you insist on using tools to detect AI-generated text in student work at least do so in an open and transparent way”
    • (Liang et al.) – “GPT detectors frequently misclassify non-native English writing as AI generated”
    • | Computers and Education: Artificial Intelligence (Fleckenstein et al.) – “Generative AI can simulate student essay writing in a way that is undetectable for teachers”
  • Constructive approaches to academic integrity & assessment validity
    • (Camosun College Library)
    • – and see his detailed explanation and examples for
  • Examples of tools used to fool AI detectors

Perspectives & discussion material on AI in learning/teaching

  • (Cognitive Resonance)

Teaching with and about AI

AI & ethics
  • – AI & Bias (UCLA Institute for Technology, Law & Policy)
  • AI Ethics & Policy News - Examples of AI ethics issues covered in the news, categorized by issue area
  • – description and examples of application of Hugging Face’s framework for ethical AI (Rigorous; Consentful; Socially Conscious; Sustainable; Inclusive; Inquisitive)
Critical source evaluation
Teaching writing
  • (MLA-CCCC Joint Task Force on Writing and AI)
  • (MLA-CCCC Joint Task Force on Writing and AI)
Technical guidance for using AI in teaching
Other teaching resources
  • (metaLAB (at) Harvard)
  • (Ethan R. Mollick, Lilach Mollick)
  • (University of Maine) - partially crowdsourced but curated collection of links (with brief descriptions) to teaching resources and strategies
  • (MLA-CCCC Joint Task Force on Writing and AI)
  • – discussion thread with example prompts for using genAI to assist in different learning scenarios (requires joining POD AI in Education discussion group)
Books on teaching & AI
  • (Troy Heaps)
  • (José Antonio Bowen and C. Edward Watson – AAC&U)

Guides for students

  • (Elon University & AAC&U)

AI in libraries & research

  • Research tools
    • AI-Based Literature Review Tools (Texas A&M University Libraries)
Citation
  • (U Calgary Libraries) – summaries of APA, Chicago and MLA styles + other sources of citation guidance
  • (MLA Style)
  • (Chicago Manual of Style)
  • (APA Style)
Transparency & disclosure

Clearinghouses of information/resources

  • (Lance Eaton)
  • (Anna Mills) – see in particular
  • Policies (see “Environment, frameworks & examples for policy development” below)
AI in libraries
  • (AI projects, data sets, resources for libraries, archives and museums)
  • (Florida International University Libraries)
Tools
  • (Dan Fitzpatrick)
  • (Anthropic/Claude)
Products & licensing

Discussion & keeping up

Discussion with global peers
  • (requires EDUCAUSE account – ҵ faculty & staff can create accounts)
  • AI & Libraries
    • (meeting notes and recordings)
Blogs & newsletters
  • (Lance Eaton)
  • (Ethan Mollick)
  • newsletter (Amherst College)
  • (Jeremy Caplan)

Training opportunities

  • – free self-paced course

Campus-level AI initiatives & policy development

We’ve found the resources listed here useful in shaping the AI@ҵ initiative. ҵ colleagues may find these useful as well in thinking about how they can contribute to the initiative or its goals.

Developing campus-level strategy and initiatives
  • Broad principles for AI in higher ed
    • (UN lnternet Governance Forum, Kyoto, Oct 2023)
    • (Russel Group of leading UK universities)
  • Organizational assessment
    • (Joe Sabado)
  • Developing a strategy
    • (MIT strategy guide for addressing AI at higher ed institutions)
Environment, frameworks & examples for policy development
  • Governmental policy frameworks & recommendations
    • Europe
      • EU AI Act
    • France
    • United States
  • Higher ed frameworks & recommendations for policy development
    • – based on this study:
  • Commentary & analysis on higher ed AI policy
    • (IHE)
  • Library & publishing organizations’ guidance on AI
    • (ARL)
  • Examples of existing policies
    • Lists of existing policies
    • Specific examples of note
      • (proposed) (metaLAB (at) Harvard)
      • (College Unbound / Lance Eaton)
      • See “ҵ-relevant examples of campus-level initiatives” below
  • Templates for developing your own policy
    • (Joe Sabado)
Academic integrity policies

(See also the section above “Communicating with students about AI & AI use - Academic integrity & AI use”)

  • Using human rights principles to guide academic integrity policies
    • : future-proofing human rights protection in the era of AI (Council of Europe Commissioner for Human Rights)
    • (Sarah Eaton)
  • ” (Sarah Eaton)
  • (Josh Brake)
AI literacy & curricular integration

  • AI literacy frameworks

    • (Barnard College’s scaled framework for moving up a scale: Understand → Use → Analyze → Create)

    • (MLA & CCCC)

    • AI Literacy Model - Practitioner-Scholar Approach Template (Joe Sabado)

    • (EDUCAUSE AI Literacy Programs for Faculty, Staff, and Students Working Group)

    • - Updates to their gen ed learning outcomes are highlighted in : info lit learning outcomes now mention evaluating *origins* of information and understanding ethical dimensions of information work using AI

    • rs (UNESCO) - both frameworks emphasize a human-centered mindset and AI ethics, as well as understanding of AI in order to use it in learning or teaching contexts

  • AI across the curriculum

    • (Southworth et al.) – includes U Florida’s AI literacy framework

    • (IHE) (Kathleen Landy)

ҵ-relevant examples of campus-level initiatives
  • Liberal arts colleges
    • Amherst College:
    • Davidson College:
  • Larger universities
    • (University of Toronto)
  • AMICAL Consortium institutions
    • (recording and links to resources mentioned)
    • (Forman Christian College)
    • (AU Kuwait Center for Teaching Excellence)
Staff guidelines on AI use – examples