Upcoming Reading Group on Critical AI

I have been spending a lot of time reading and learning about various aspects of AI, and its impact on education.(I will share more about my training and explorations in my next blog post). I recently saw the following advertised by William Frey on LinkedIN. This looks like an excellent opportunity to continue explorations (especially since I have already read some of the books), so I signed up!


I am organizing and facilitating a virtual learning community on Critical Studies of Artificial Intelligence, beginning in February 2026. Together, we will be reading 7 books over 15 weeks.

No prior knowledge is required, just the commitment to reading the books and discussing with others in the space. Please feel free to share with others (no academic affiliation is needed either, all are welcome). If you are interested in participating, please fill out the form at this link: https://lnkd.in/ejwDnBYH

If you have any questions or concerns that are not answered by the information in this message and the form, please feel free to contact [him] directly (williamfreyx@gmail.com).

Highlights from EDUCAUSE 2025

The EDUCAUSE Annual Conference took place last week in Nashville, TN.  

It advertises itself as "THE event where professionals and technology providers from around the world gather to network, share ideas, grow professionally, and discover solutions to today’s challenges. It’s the largest gathering of your peers…people you can relate to, learn from, and stay connected to throughout the year".

It’s not too late to register for the online version of the conference that takes place later this week (and will feature sessions from the in-person conference in Nashville as well as exclusive new content): https://events.educause.edu/annual-conference/attend/online-conference-registration (IU is an institutional member of EDUCAUSE; you can access any of their public facing sites using your IU credentials through the SSO option if asked).

Below are a few of the featured and general session recordings from the live face-to-face meeting in Nashville. 

_ Unmasking AI_ Protecting What Is Human in a World of Machines



Presenter: Joy Buolamwini
Abstract: Groundbreaking researcher, Dr. Joy Buolamwini, shares an illuminating investigation into the harms and biases of artificial intelligence. In this session, Dr Joy will explore AI in decision-making, aligning AI with fairness and organizational values and leading boldly in an AI-driven world.

Augmented Intelligence


Presenter: Jules White
Abstract: Generative AI represents a new paradigm in computing—one that centers human ideas as the starting point for computational action. Rather than relying on traditional programming, this approach allows people to express complex goals in natural language, making it possible to “compute on thought” across disciplines. This talk explores how generative AI reshapes our relationship with technology by enabling systems that respond to intent, refine outputs through dialogue, and integrate diverse tools and data. The result is a more interdisciplinary model of innovation where domain experts, creatives, and technologists collaborate through shared language. At its core, this shift is about augmented intelligence: amplifying human creativity, reasoning, and problem-solving—not replacing it—so that computing becomes a more fluid extension of human thought.





The Belonging Imperative


Presenter: Keith McIntosh
Abstract: Belonging is no longer optional — it is the defining imperative for today’s leaders! In a time when retention, engagement, and culture are under strain, leaders must intentionally design environments where people feel seen, supported, and connected. This session, led by Dr. Keith W. McIntosh, a nationally recognized higher education CIO, award-winning leader, scholar, practitioner, and thought partner on inclusive leadership and belonging, positions leaders as architects of belonging, responsible for shaping the systems, spaces, and practices that determine whether employees thrive or disengage. We’ll discuss the science of belonging and its connection to well-being, performance, and retention, while also highlighting real-world examples of leaders who embody these values. Research consistently shows that employees who feel a strong sense of belonging are more engaged, more loyal, and more productive. We’ll explore why belonging must sit alongside strategy and innovation on every leader’s agenda, and how it creates legacies that outlast titles or tenures. Participants will leave with a deeper understanding and practical strategies to embed belonging into daily leadership practices, building communities where people want to stay, grow, and contribute.

Resilient Campuses in Turbulent Times


Presenter: Freeman Hrabowski
Abstract: Higher education professionals are experiencing an unprecedented period of turbulence, resulting from significant political, demographic, cultural, and technological changes. These broad forces range from declining enrollment to shifts in employment, federal and state policy changes, and technological changes, including the ascent of generative AI. Rather than give into despair, institutional leaders, and the teams they lead can instead choose resilience, the ability to remain focused and effective in the face of these and other challenges. To address these ongoing challenges, institutions must become increasingly effective in the use of data, analytics, and AI to increase student success and ensure that people are the highest priority. The talk will highlight the importance of vision, openness, resilience, courage, passion, and hope.

Beyond the Hype: A Practical GenAI Resource Guide for Faculty in Technical Disciplines

As faculty that teach technical disciplines, you are in a unique position. You aren’t just figuring out how to use Generative AI; you are teaching the students who will build, deploy, and critically evaluate these tools for years to come.

The challenge is twofold:

  • How can you leverage AI to improve your own teaching (e.g., create coding examples, debug assignments, or design better projects)?

  • How can you effectively integrate AI into your curriculum as a core competency (e.g., teach prompt engineering, model limitations, and AI ethics)?

The internet is flooded with AI resources, and it’s impossible to sift through them all. This post is a practical, curated guide to help you find the most useful resources for your courses without the noise.

Start with IU: Key Local Resources

Before diving into the wider web, start with the excellent resources available directly from IU. These provide the foundational context and policies for our community.

Generative AI 101 Faculty Resources
Description: An overview of the GenAI 101 Course available to all at IU. Also includes a syllabus insert that can be used to promote the course to students.

Kelley School of Business “AI Playbook”
Description: A “living guide” developed by the Kelley School for faculty on the use of generative AI in teaching, grading, and research. It outlines shared values and emphasizes that faculty expertise remains central.

When to use: When you want faculty-facing guidance on when and how to use generative AI in assessments, course design, and feedback workflows.

A Quick Starting Point: Three Actionable Resources

If you want to branch out, here are three high-value resources to review in 10 minutes or less.

  1. For Your Curriculum: Teach CS with AI: Resource Hub for Computer Science Educators

    • What it is: A hub specifically for integrating AI into CS courses. It includes lesson plans, project ideas, and pedagogical strategies for teaching AI in computing.

    • When to use: When you’re not just using AI, but actively teaching AI concepts, ethics, or applications within a CS or Informatics course.

  2. For Your Pedagogy: Harvard University:“Teaching with Gen-AI” resources

    • What it is: High-level guidance from Harvard on course design, with excellent case studies and strategies for handling risks like hallucinations and superficial reasoning.

  3. When to use: Use this before the semester starts. It’s perfect for designing your syllabus, setting AI policies, and building responsible use guidelines into your course from day one.

  4. For Your Students (and You): AI for Education: “Effective Prompting for Educators”

    • What it is: A focused guide on how to write better prompts. It includes frameworks (like the “5 S Framework”) that are perfect for teaching students a structured approach to “prompt engineering.”

    • When to use: When you want to move students beyond simple “ask-and-receive” and teach them how to partner with AI to get better, more reliable, and more complex results.

The Deep Dive: A Curated Resource Library

For those with more time, here is a more comprehensive list organized by task.

1. How to Use AI in Your Classroom (Pedagogy & Assignments)

2. Helping Students (and You) Get Better at Prompting

  • AI for Education: Prompt Library

    • Description: A comprehensive, searchable collection of ready-to-use prompts and templates specifically for educators.

    • When to use: When you need quick, plug-and-play prompt templates for lesson plans, student tasks, or administrative work.

  • More Useful Things — Prompt Repository for Educators

    • Description: A repository of prompts for instructor aids and student exercises, curated by researchers Ethan and Lilach Mollick.

    • When to use: When you want tested, inspiring prompt sets, especially for idea generation or in-class activities.

  • Anthropic Prompt Library 

    • Description: Anthropic’s (maker of Claude) public library of optimized prompts for business, creative, and general tasks.

    • When to use: When you want to show students (or yourself) “what good prompting looks like” from an industry leader.

3. How to Teach AI in Your CS/InF Courses (Curriculum & Literacy)

  • Teach CS with AI: Resource Hub for Computer Science Educators

    • Description: A hub dedicated to integrating AI topics, tools, and teaching strategies in CS courses.

    • When to use: Use when teaching a CS course and you want to integrate AI content (topics, labs, projects) directly.

  • metaLAB at Harvard: The AI Pedagogy Project / AI Guide

    • Description: A curated site with assignments and projects to integrate AI in pedagogical practice, focused on critical thinking.

    • When to use: When you are designing a module on AI literacy, critical AI thinking, or assessing students’ interaction with AI tools.

  • Ideeas Lab: Teaching & AI resources

    • Description: A resource hub with teaching materials and tools, particularly aimed at engineering and technical fields.

    • When to use: When you want resources specifically tailored for engineering domains that integrate AI in assignments.

  • AI for Education: “Generative AI Critical Analysis Activities

    • Description: Classroom activities to help students critically examine AI outputs, ethics, and limitations.

    • When to use: When you want to design modules around AI ethics or have students evaluate AI rather than simply use it.

4. Taking it Further: Building Your Own AI Tools

5. Professional Development & Staying Current

  • IBM Skills Build for Educators: College Educators resources

    • Description: A professional development site offering modules and training materials to build AI fluency and integrate digital skills into teaching.

    • When to use: When you want a structured PD path for yourself or want to build a course around AI literacy and workforce readiness.

  • University of Maine: LearnWithAI initiative

    • Description: A practical, “how-to” oriented site for faculty on integrating AI into courses.

    • When to use: Use when you want a site focused on faculty development and practical course integration.

  • Future-Cymbal Notion Page: Shared collection of AI-Teaching Resources

    • Description: A collaboratively curated Notion page of ideas, links, frameworks on AI in education; less “formal guide,” more open resource aggregation

    • When to use: Use when you want to browse a broad, ever-updating set of ideas rather than a polished handbook.

  • AI Resources – Lance Eaton

    • Description: It collects a wide variety of resources for educators around generative AI in the classroom — such as sample syllabus statements, institutional policy templates, teaching ideas, and faculty development materials.

    • When to use: When you are designing or revising your course syllabus and need clear language about how you will (or won’t) allow AI tools in student work.

  • Newsletters for Staying Current:

    • The Rundown -Daily newsletter summarizing AI news across research, policy, and industry.

    • The Neuron – Broad coverage of emerging AI trends and commentary, often with education-adjacent insights.

    • The Batch – Weekly deep dives into AI research, tools, and development—ideal for those following the tech side.

    • The Algorithmic Bridge | Alberto Romero – Thoughtful essays analyzing AI’s social, ethical, and educational impact.

    • Everyday AI Newsletter – Daily newsletter (and accompanying podcast) aimed at making AI accessible to “everyday people” whether educators, professionals, or non-tech specialists.

Conclusion: Start Small, Start Now

You don’t need to redesign your entire curriculum overnight. The best approach is to start small.

Pick one thing to try this month. It could be using a prompt library to help you write a coding assignment, adapting a syllabus policy, or introducing one critical analysis activity in a senior seminar. By experimenting now, you’ll be better prepared to lead your students in this new, AI-driven landscape.

Did I miss a great resource? Leave a comment and let me know!

Teaching for Integrity in the Age of AI: From Compliance to Culture

Inspired by Chapter 2 of The Opposite of Cheating: Teaching for Integrity in the Age of AI by Tricia Bertram Gallant and David Rettinger

Academic integrity is not a checklist or compliance form. It is a living culture shaped by what we model, how we design, and the conversations we hold with our students. Gallant and Rettinger remind us that integrity is cultivated through transparency, design, and dialogue, not surveillance or punishment. The real challenge now is how to teach integrity in an age where AI is everywhere.

The U.S. Department of Education’s 2023 report, “Artificial Intelligence and the Future of Teaching and Learning”, encourages educators to treat AI as a design opportunity for advancing human-centered learning, not a threat to academic honesty. Recent data highlight the urgency of this work. According to the Higher Education Policy Institute’s 2025 Student AI Survey, 92% of undergraduates report using generative AI tools, up from 66% the year before. A Guardian report found that AI-related misconduct cases have tripled since 2023. The takeaway is clear: integrity education has to evolve alongside AI literacy.

2025 Snapshot: AI & Academic Integrity

Use and Attitudes

  • Over 85% of undergraduates use GenAI tools (Inside Higher Ed, HEPI 2025)

  • 61% of students want clear, course-level AI policies

  • 33% of students were concerned about being accused of plagiarism or cheating (Campus Technology)

  • While 45% believe using AI for editing is “acceptable academic support”

Institutional Responses

Faculty Trends

  • A significant gap exists between student and faculty adoption: only 61% of faculty report using AI in teaching, and of those, a large majority (88%) do so minimally (ASEE AI Training 2025 led by Drs. Adita Jori and Andrew Patz).

  • 82% of instructors use GenAI for feedback or rubric design (EDUCAUSE 2025 AI Landscape Study)

  • Detection tools now use watermarking and metadata tracing, but false positives remain a major concern (arXiv 2025)

Model Integrity

Students notice how we work. They learn from the way we check our sources, document decisions, and acknowledge mistakes. Modeling integrity starts with transparency.

As the EDUCAUSE 2025 AI Landscape Study notes, many universities are investing in training that helps faculty engage AI responsibly. Modeling integrity now means showing how to use AI intentionally, not avoid it.

This aligns with findings from Gu and Yan’s 2025 meta-analysis, which showed that students benefit most when teachers scaffold AI use and talk openly about it. When instructors frame AI as a learning partner, not a shortcut, students develop stronger judgment and accountability.

Make Integrity Explicit

Integrity should show up as often in our discussions as it does in our policies. When we talk about it before projects, during collaborations, and after challenges, students begin to see ethics as part of the learning process.

Tricia Bertram Gallant and David Rettinger emphasize that ethical behavior thrives when it’s designed into the experience. Singer-Freeman, Verbeke, and Barre (2025) found that students across all academic levels want clear guidance on what’s acceptable AI use. If we make expectations explicit, we replace anxiety with understanding.

A recent MDPI review on Generative AI and Academic Ethics reinforces this point, noting that while GenAI can enhance engagement and efficiency, it also increases risks to originality and ethical reasoning.

Use Clear, Simple Language (The Social Institute)

Students need to understand A.I. policies to be able to follow them. That means avoiding jargon and overly technical language.

Instead of: “A.I. assistance must align with established academic integrity principles.”

Say: “You may use A.I. for brainstorming ideas, but not for writing entire sections of code or essays.”

Establish consistent Rules Across Departments or Schools

One of the biggest sources of confusion is inconsistent enforcement when it comes to A.I. rules. Departments or schools can develop a universal A.I. guidelines that applies to all instructors, rather than allowing individual educators to set conflicting rules. Over half of students (58%) report that their school or program has a policy, but a substantial number (28%) say it differs, with some courses or professors having a policy and some not (Forbes 2025). Consider creating an instructor handbook outlining departmental or school-wide A.I. best practices to make sure they are consistently communicated to students.

Frame Integrity Positively

Instead of framing integrity around rules, frame it around growth. Students respond better when they see ethical choices as part of their professional development.

A Packback editorial on academic integrity in 2025 argues that punitive detection systems often erode trust and discourage learning. When faculty shift from surveillance to conversation, integrity becomes something students take ownership of, not something they fear.

Clarify Expectations

Ambiguity creates rationalization. In the age of AI, clarity is an act of fairness.

The National Centre for AI’s 2025 student study found that first-year students, in particular, feel confused about when and how AI use is acceptable. Faculty can address this by defining boundaries early and discussing examples. Transparency about tools, citations, and documentation helps students learn discernment.

Research from arXiv’s 2025 watermarking study adds that while detection tools are improving, they still make errors. Open conversations about what these systems can and cannot do build trust and understanding. Institutions like MIT and Duke University (22 minute mark) provide sample policy language for faculty to adapt. These statements define what “appropriate help” means and require students to cite AI contributions when used. Clarity transforms anxiety into accountability.

Normalize Conversations About Ethics

Ethics belongs in everyday learning. Conversations about bias, authorship, and data use should happen alongside technical instruction.

A 2025 study on synthetic media ethics found that students value open discussions about deepfakes and misinformation but often lack the frameworks to evaluate them. Integrating these discussions into our teaching helps students connect ethics to both academic and professional practice.

Use the Syllabus as a Moral Document

The syllabus sets the tone for integrity. Transparent grading policies, clear AI statements, and flexible revision options communicate fairness and care.

Universities are redesigning their syllabi and assessments to support “authentic learning” instead of reactive policing. The University of Melbourne’s Assured Learning model and the UCL Education AI Initiativeare leading examples, focusing on oral exams, reflective portfolios, and transparent assessment design.

Respond to Misconduct Constructively

When integrity violations occur, they can become moments for growth. Reflection, accountability, and dialogue teach more than punishment ever could.

The Packback 2025 Integrity Report encourages “growth-oriented remediation,” noting that many flagged cases stem from confusion, not intention. At Indiana University, we can uphold policy while still approaching each case as a learning opportunity.

Building a Culture of Integrity

Integrity thrives when it’s shared across the institution. Faculty, staff, and students each play a role.

The University of New South Wales’ 2025 partnership with OpenAI illustrates this shift: giving staff controlled access to ChatGPT within a responsible use framework. When universities model integrity through their own practices, students learn that ethics is not a barrier to innovation—it’s the framework that sustains it.

Final Thought

Teaching for integrity in the age of AI is about creating conditions where honesty becomes the natural choice. When we model transparency, design for trust, and engage in open dialogue, we teach more than content—we teach character.

As Amanda McKenzie, Director of Academic Integrity at the University of Waterloo, Canada, shares, “Integrity is not the opposite of cheating. It’s the presence of purpose.” When that purpose runs through our teaching, policies, and partnerships, we do more than protect academic standards. We prepare students to lead with integrity in a world increasingly shaped by AI.

Possible ways to improve attendance

One of the most frequent concerns I hear is, “My students just aren’t coming to class.” With so much content available online, recorded lectures at their fingertips, and the sense of distance that can come with large classes, this challenge is becoming more common and more complex. In this post, I will look at some of the more popular reasons reported for students not attending class and share practical, evidence-based ways to re-engage students in the classroom.

The Anonymity Epidemic: When Students Feel Like Just Another Face

Many students, particularly in large enrollment courses, feel anonymous. They don’t believe their individual presence makes a difference, leading to a disengagement from the classroom community. This isn’t just a large-class problem; it arises when students lack meaningful connections with instructors, TAs, or even their peers. Overcoming this anonymity is key to fostering a sense of responsibility and belonging.

Strategies to Combat Anonymity:

  • Be Present Before Class: Arriving early to chat informally with students is a simple yet powerful way to build rapport. Ask about their weekend, recent movies, or even their experience with the last assignment. These small gestures humanize you and create a connection.

  • Active Engagement is Key: Design activities that actively involve students with the material. Pose intriguing questions, facilitate brief peer discussions, or utilize classroom response systems like TopHat https://uits.iu.edu/tophat/index.html to “vote” on responses. This transforms passive listening into active participation, fostering an intellectual community.

  • Learn Their Names (or Try): Even the attempt to learn student names is deeply appreciated. Ask for names when students speak and use them in your response. Consider using a photo roster from Canvas to help you put names to faceshttps://toolfinder.iu.edu/tools/iu-photo-roster. A study in a high-enrollment biology course found that students’ perception of their instructor knowing their name was highly correlated with a sense of belonging, even though the instructors didn’t know every student’s name https://www.lifescied.org/doi/full/10.1187/cbe.16-08-0265 This suggests that the effort and intention behind using a student’s name are just as important as the memorization itself. For more strategies see: https://teachinginhighered.com/podcast/how-to-learn-students-names/

  • Cultivate Peer Connections: Encourage students to get to know each other. In in Relationship-Rich Education: How Human Connections Drive Success in College(Felten & Lambert, 2020) https://iucat.iu.edu/catalog/19430355mention that students benefit when they are guided in how to connect, not just told to “work together.” On the first day, have them introduce themselves to those around them. Additional strategies might include teaching collaboration skills, establishing norms for group work, or prompting reflection on what makes a partnership effective. If you use group work, rotate group members throughout the semester. Periodically have students shift seating to broaden their peer interactions.

  • Personalized Feedback (Even in Large Classes): While challenging, finding ways to provide even small amounts of personalized feedback on assignments can significantly reduce feelings of anonymity. This could be through targeted comments on a rubric or brief, individualized responses to discussion forum posts. In large classes, it’s impossible to give every student a paragraph of detailed feedback each week, but you can make feedback feelpersonal by thinking in layers. I like to frame it as macro, meso, and micro feedback. At the macro level, I share short announcements summarizing class-wide trends; what students are doing well, what’s tripping them up, and a few standout examples. At the meso level, I provide targeted feedback to lab sections, project teams, or discussion groups that speaks directly to their shared progress. Then at the micro level, I use rubrics and comment banks to individualize comments just enough to sound human…adding a student’s name or referencing something specific from their work. It’s not about writing more; it’s about being intentional with how students experience the feedback they receive.

The “Why Bother?” Dilemma: Lack of Incentive, Relevance, and Engagement

Students often skip lectures if they perceive the content as readily available elsewhere, not directly relevant to their goals, or simply boring.

 

Strategies to Create Incentive and Relevance:

  • Incentivize Attendance: Leverage students’ natural focus on grades. Make attendance a component of the grade, or administer short, low-stakes quizzes at the beginning of class using tools like Canvas or TopHat.

  • Design Slides to Drive Presence:Explicitly state that your posted slides are incomplete. Design them as skeletal frameworks, requiring students to annotate and fill in critical explanations and examples during lecture. This creates a clear value proposition for attending.

  • Debunk the “Notes from a Peer” Myth:Directly address the inadequacy of relying solely on peer notes or even AI-generated summaries. Emphasize that context, instructor insights, and the organic flow of a live lecture cannot be fully replicated.

  • Connect to Their World: Embed examples, applications, and topics that resonate with students’ fields of study and current cultural interests. Utilize Canvas Course Analytics, Reports and Dashboardsand/or  pre-course surveys to understand your student demographics and tailor examples accordingly.

  • Pique Interest from the Start: Begin lectures with a challenging question, an intriguing anecdote, or a real-world problem that immediately grabs attention and motivates sustained engagement.

  • Convey Your Enthusiasm: Your passion for the subject is contagious! Share personal stories, recent discoveries, and your excitement for the discipline. Voice and body language naturally convey this enthusiasm.

Overcoming Information Overload and Misaligned Expectations

Sometimes, students skip because they feel overwhelmed, confused by lecture goals, or perceive the lecture as redundant to textbook material.

Strategies for Clarity and Complementary Learning:

  • Chunk Your Lectures & Re-engage:Recognize that typical attention spans are 10-20 minutes. Plan your lectures in shorter chunks, incorporating varied activities every 15-20 minutes to re-engage attention (e.g., questions, visuals, demonstrations, group work, videos). Consider attending the upcoming Active Learning Block Party for Large Classrooms sponsored by CITL for engagement ideas.

  • Complement, Don’t Reiterate, the Textbook: Use class time to expand on readings, provide alternative perspectives, facilitate problem-solving, or have students generate their own examples. The lecture should offer something the textbook doesn’t.

  • Provide Unique Experiences: Bring in guest speakers, conduct live demonstrations of code or hardware, or share cutting-edge research and innovations that students wouldn’t encounter elsewhere that connect with course content.

  • State Your Goals Clearly: Explicitly articulate the learning objectives for each lecture. Use these goals as “mileposts” to help students track their progress and understand the desired outcomes.

  • Share the Organization: Provide an outline, agenda, or visual representation of the lecture’s structure. Don’t assume novices will automatically see the logical connections among concepts.

  • Encourage Support Services: If you identify students struggling with academic or non-academic demands, refer them to appropriate support services like Academic Development, the Counseling Center, or Student Health.  Student Resource Slideshow.pptx

  • Support Language Learners: For students whose first language is not English, refer them to resources like the Office of International Services which offers drop-in English tutorials for second language students https://ois.iu.edu/get-involved/english-tutorials/index.html

  • Provide Recordings (Strategically):While recordings can reduce attendance, they are a valuable accessibility tool. If you record, emphasize that the recording is a supplement for review or for those with legitimate absences, not a substitute for live engagement. Consider how you might make the live session distinctly more valuable than the recording (e.g., interactive elements through PlayPosithttps://uits.iu.edu/services/technology-for-teaching/instruction-and-assessment-tools/playposit/index.html, Q&A).

The Power of Visuals and Storytelling

In fields like Computer Science and Engineering, abstract concepts can be difficult to grasp. Visuals and real-world narratives can significantly enhance comprehension and engagement.

Additional Tips:

  • Integrate Visualizations: When explaining complex algorithms, data structures, or system architectures, use diagrams, flowcharts https://miro.com/, and animations  Show, don’t just tell. Consider generating some of these visualizations on the fly with your students!

  • Tell Stories of Impact: Frame technical concepts within the context of real-world problems they solve or innovative applications. How did this algorithm enable a new technology? What societal problem does this data science technique address?

  • Live Coding Demonstrations: For programming or data manipulation courses, live coding is incredibly effective. It allows students to see the process, observe debugging strategies, and ask questions in real-time. Make sure to slow down and explain your thought process.

  • Guest Speakers from Industry: Invite professionals from relevant industries to share how the concepts taught in class are applied in their day-to-day work. This provides tangible career relevance.

By adopting these evidence-based strategies, faculty can transform their lectures from passive information dissemination into vibrant, engaging learning experiences that students genuinely want to attend. The goal isn’t just to fill seats, but to foster deeper learning and a stronger connection to the academic community.

 

Building a Framework for Academia-Industry Partnerships and AI Teaching and Learning Podcasts

In March, I shared the an overview of the  “Practitioner to Professor (P2P)‘ survey that the CRA-Education / CRA-Industry working group analyzed. They recently released a report titled Breadth of Practices in Academia-Industry Relationships which explores a range of engagement models from research partnerships and personnel exchanges to master agreements and regional innovation ecosystems.

Key Findings and Observations

The report organizes its findings from the workshop into three categories: observations, barriers, and common solutions:

  • Observations A major theme was the critical need to embed ethical training into AI and computing curricula through both standalone courses and integrated assignments. It was noted that while academia is best suited to drive curriculum development, input from industry is essential to ensure the content remains relevant to real-world applications.

  • Barriers Key barriers to successful collaboration were identified, including cultural differences and misconceptions between academic and industry partners. For instance, industry’s focus on near-term goals can clash with academia’s long-term vision. A significant practical barrier is the prohibitive cost of cloud and GPU hardware, which limits students’ experience with cloud and AI development tools.

  • Common Solutions Effective solutions include the fluid movement of personnel between organizations through internships, co-ops, sabbaticals, and dual appointments. Streamlined master agreements at the institutional level also help facilitate research collaborations by reducing administrative friction.

Strategies for Research Collaboration

The report outlines a multi-level approach to enhancing research partnerships:

  • Individuals Faculty and industry researchers can initiate relationships through internal seed grants, sabbaticals in industry, dual appointments, and by serving on industry advisory boards.

  • Departments Departmental leaders can foster collaboration by strategically matching faculty expertise with industry needs, offering administrative support, and building a strong departmental brand with local industry.

  • University Leadership Senior leaders can address systemic barriers by creating a unified, institution-wide strategy, developing flexible funding models, and implementing master agreements to streamline partnerships.

  • Regional Ecosystems The report emphasizes the importance of universities partnering with local industries and startups to build thriving regional innovation ecosystems, which can drive economic development and secure government support.

Education and Workforce Development 

With the rise of generative AI, the report highlights an urgent need for universities and industry to partner on education.

  • Curriculum Adaptation Computing curricula need to be updated to include foundational concepts in DevOps and scalable systems, which are often not part of the core curriculum. While AI literacy is essential, the report suggests a balance, with 80% of instruction remaining focused on core computer science skills. Ethical reasoning should be integrated throughout the curriculum, not just in a single course.

  • Workforce Programs To meet industry demands for job-ready graduates, the report advocates for university-industry partnerships in co-op programs, internships, and capstone projects. It also points to the need for universities to offer flexible programs like certificates and online courses to help upskill and reskill the existing workforce.

Recommendations

The report concludes with five main recommendations for universities, industry, and government:

  1. Enhance research impact by combining academia’s long-term vision with real-world problems from industry. This can be achieved by embedding faculty in industry and industry researchers in universities.

  2. Leverage the convening power of universities to build partnerships that benefit the wider community, using mechanisms like industrial advisory boards and research institutes.

  3. Accelerate workforce development by aligning university programs with regional innovation ecosystems and having industry invest in talent through fellowships and internships.

  4. Deliver industry-relevant curricula grounded in core computing principles, and collaborate with industry experts to co-design courses in high-demand areas like AI and cloud computing.

  5. Establish new incentives and metrics to recognize and reward faculty for their contributions to industry partnerships in promotion and tenure evaluations.

AI Teaching and Learning Podcasts:What If College Teaching Was Redesigned With AI In Mind?

https://learningcurve.fm/episodes/what-if-college-teaching-was-redesigned-with-ai-in-mind

A former university president is trying to reimagine college teaching with AI in mind, and this year he released an unusual video that provides a kind of artist’s sketch of what that could look like. For this episode, I talk through the video with that leader, Paul LeBlanc, and get some reaction to the model from longtime teaching expert Maha Bali, a professor of practice at the Center for Learning and Teaching at the American University in Cairo.

The Opposite of Cheating Podcast

https://open.spotify.com/show/5fhrnwUIWgFqZYBJWGIYml

(Produced by the authors of the book with the same name) the podcast shares the real life experiences, thoughts, and talents of educators and professionals who are working to teach for integrity in the age of AI. The series features engaging conversations with brilliant innovators, teachers, leaders, and practitioners who are both resisting and integrating GenAI into their lives. The central value undergirding everything is, of course, integrity!

Teaching in Higher Ed podcast, “Cultivating Critical AI Literacies with Maha Bali”.

https://teachinginhighered.com/podcast/cultivating-critical-ai-literacies/

In the episode, host Bonni Stachowiak and guest Maha Bali, a Professor of Practice at the American University in Cairo, explore the complexities of integrating artificial intelligence into higher education.

Bali advocates for a critical pedagogical approach, rooted in the work of Paulo Freire, urging educators to actively experiment with AI to understand its limitations and biases. The discussion highlights significant issues of cultural and implicit bias within AI systems. Bali provides concrete examples, such as AI generating historically inaccurate information about Egyptian culture, misrepresenting cultural symbols, and defaulting to stereotypes when prompted for examples of terrorism.

The Actual Intelligence podcast

speakswith Dr. Robert Neibuhr from ASU regarding his recent article in Insider Higher Ed: “A.I and Higher Ed: An Impending Collapse.” Full Podcast: https://podcasts.apple.com/us/podcast/is-higher-ed-to-collapse-from-a-i/id1274615583?i=1000725770519

with Bill Gates having just said that A.I. will replace most teachers within ten years, it seems essential that professional educators attune to the growing presence of A.I. in education, particularly its negative gravitational forces.

The Guide on the Side: Coaching STEM Students in Problem-Solving

From Manager to Mentor: A Practical Strategy for AI Development

As faculty, we know that working effectively with our Assistant Instructors (AIs) is key to a successful course. In last week’s post on Best Practices for Working with Assistant Instructors,” I highlight the importance of mentorship and creating professional development opportunities. But what does that mentorship look like in practice?

One of the most impactful ways to mentor our AIs is to equip them with high-leverage teaching strategies. Instead of just managing their grading, we can teach them how to teach. A powerful approach for this is the Guide on the Side philosophy, which shifts the AI’s role from a simple answer-key to a learning coach.

The Guide on the Side: Coaching STEM Students in Problem-Solving

It’s a familiar scene in any STEM lab or office hour: a student, staring at a screen, is utterly stuck. For new teaching assistants (Associate Instructors, or AIs), the temptation is strong to take the shortcut; to grab the keyboard, write the line of code, or simply provide the answer. But while this solves the immediate problem, it bypasses a crucial learning opportunity.

This is where the Guide on the Side approach comes in. It’s a teaching philosophy that equips new AIs with practical strategies to coach students through the problem-solving process rather than solving problems for them. For faculty in STEM, empowering your AIs with these skills can transform your students’ learning experience. 

Why This Shift in Pedagogy Matters

Across STEM disciplines, students frequently encounter “sticking points” moments of cognitive friction where the path forward isn’t obvious. If an instructor or AI simply hands over the solution, the student leaves with a single answer but no transferable skill. They learn to be dependent on an external expert.

By contrast, an instructor who guides the process models resilience, inquiry, and expert reasoning. The student leaves not only with a solution but with strategies they can apply to the next problem, and the one after that. They learn how to think.

Putting Theory into Practice: Activities for Your AIs

Faculty can use these activities in their own training sessions to help AIs develop a coaching mindset:

  • “Sticking Point” Brainstorm: In a think-pair-share format, AIs identify the most common places their students struggle. This builds a shared awareness of teaching challenges and normalizes the experience.

  • Scenario Analysis: AIs compare two contrasting dialogues: one where the AI gives the answer directly, and another where the AI uses Socratic questioning to lead the student to their own solution.

  • Questioning Roleplay: In pairs, AIs practice how to respond with guiding questions when students make common statements like, “I’m totally lost,” or “Can you just tell me if this is right?”

A Simple Framework for Modeling Expertise

A core strategy of this approach is teaching AIs to make their thinking visible. Experienced problem-solvers naturally follow steps that are often invisible to novices. Encourage your AIs to narrate their own problem-solving process explicitly using a simple four-step framework:

  1. Understand: Restate the problem in your own words. What are the inputs, the desired outputs, and the constraints?

  2. Plan: Outline possible approaches. What tools, algorithms, or libraries might be useful? What are the potential pitfalls of each approach?

  3. Do: Execute the plan step by step, narrating the reasoning behind each action. (“First, I’m going to create a variable to hold the total because I know I’ll need to update it in a loop.”)

  4. Reflect: Test the solution. Does it work for edge cases? Could it be more efficient? Are there alternative ways to solve it? 

This explicit modeling teaches students how to think, not just what to do.

The Power of a Good Question: Building a Question Bank

Guiding questions are the primary tool of a “Guide on the Side.” They skillfully shift the cognitive work back to the student. Encourage your AIs to build a bank of go-to questions, such as:

  • To start a conversation: “What have you tried so far?” or “Can you walk me through your current approach?”

  • To prompt a next step: “What does that error message suggest?” or “What’s the very next small step you could take?”

  • To encourage deeper thinking: “Why did you choose that particular method?” or “What are the trade-offs of doing it that way?”

  • To promote reflection and independence: “How could you check your answer?” or “What would you do if you encountered a similar problem next week?” 

Navigating Common Classroom Challenges

This approach provides concrete strategies for these common moments:

  • When a student is silent: Allow for sufficient wait time. If the silence persists, break the problem down and ask a simpler, first-step question.

  • When a student is frustrated: Acknowledge their feelings (“I can see this is frustrating; these problems are tough.”) and normalize the struggle before gently re-engaging with the task.

  • When a student just wants confirmation: Instead of giving a simple “yes” or “no,” redirect with a metacognitive prompt like, “What makes you confident in that answer?” or “How could you design a test to verify that?”

Resources for a Deeper Dive 

For faculty and AIs who want to explore this pedagogical approach further, these resources are short, impactful, and highly relevant:

  • Book: Small Teaching: Everyday Lessons from the Science of Learning by James M. Lang

  • Article: Asking Questions to Improve Learning – Washington University in St. Louis Center for Teaching and Learning

  • Video: Eric Mazur’s video on Peer Instruction is a great resource for understanding how to shift from traditional lecturing to more active, student-centered learning. He effectively demonstrates the curse of knowledge and how students learning from each other can be more effective than an expert trying to explain something they’ve long ago mastered.
    His approach, where students first think individually, then discuss with peers, and finally re-evaluate their understanding, directly aligns with the principles of guiding students through problem-solving rather than just showing them the answer. It emphasizes active processing and peer teaching, which are crucial for deeper learning and developing independent problem-solvers.

The Takeaway for Faculty

The “Guide on the Side” approach aligns perfectly with evidence-based teaching practices. By encouraging your AIs to slow down, model your thinking, and use questions effectively, you help them grow from being answer keys into becoming true teaching coaches. The result is a more engaged and resilient cohort of students who leave your courses not only with solutions, but with the confidence and strategies to tackle the next challenge independently.

Best Practices for Working with Assistant Instructors

Assistant instructors (AIs) can play an essential role in supporting your course.  They support student learning, enhance faculty efficiency, and gain valuable professional development experience along the way. When managed thoughtfully, the faculty-assistant instructor partnership creates a stronger, more engaging learning environment for students and a meaningful growth opportunity for graduate students.

This following are recommendations collected from the resources mentioned below in the reference section.

Core Principles of a Strong Partnership

The faculty–assistant instructor relationship is most successful when approached as a collaborative teaching partnership. Here are some guiding principles:

  • Clear Expectations and Roles
    Both faculty and assistant instructors need a shared understanding of their responsibilities. Clarity reduces confusion and sets everyone up for success.

  • Faculty as the Ultimate Authority
    While assistant instructors play an active role in teaching and assessment, faculty ultimately carry the responsibility for the course administration duties, including grading and alignment with institutional policies.

  • Professional Development Opportunity
    Serving as an assistant instructor should be a learning experience. Faculty should connect assigned tasks to professional growth, teaching skills, and career preparation whenever possible.

  • Consistent Communication
    Regular check-ins, open conversations, and transparency help prevent misunderstandings and make problem-solving much easier when issues arise.

Setting Up for Success

Before the Semester Begins

Early connection is key. Meet with your assistant instructor before classes start to set expectations, share goals, and establish communication methods. Some items to cover:

  • Course goals and learning outcomes

  • Roles, tasks, and boundaries

  • Meeting schedules and communication channels

  • Workload expectations (respecting weekly hour limits)

  • Familiarity with technology tools

  • Academic integrity policies

  • An introduction plan so students understand the assistant instructor’s role. 

Please see https://blogs.iu.edu/luddyteach/2023/08/16/quick-tip-working-with-ais/for a checklist developed by Dr. Angela Jenks and Katie Cox , in the Department of Anthropology at the University of California, Irvine.

Having these conversations upfront helps everyone enter the semester with confidence.

During the Semester

  • Regular Meetings
    Weekly or biweekly meetings provide a chance to prepare for upcoming lessons, review grading approaches, and troubleshoot challenges.

  • Grading Consistency
    Provide rubrics and sample feedback. Calibration or grade norming activities where everyone grades the same sample are especially effective for ensuring fairness.

  • Office Hours
    Encourage assistant instructors to hold consistent and accessible office hours at different times of day to accommodate students.

  • Mid-Semester Check-In
    Use this time to gather feedback, review workloads, and adjust if necessary.

End of the Semester

Wrap up with a reflective meeting. Discuss what worked well, identify challenges, and preserve useful materials for future iterations of the course. These conversations also strengthen the mentoring relationship.

Supporting Assistant Instructor Development

Faculty aren’t just supervisors, they’re mentors. Assistant instructors benefit when faculty take the time to:

  • Coach them on teaching strategies and classroom management

  • Encourage them to set professional development goals and build a teaching portfolio if they are interested in pursuing a faculty position

  • Provide opportunities for peer observation and self-reflection

  • Direct them to school and university-wide teaching resources

By positioning the role as both service and growth opportunity, faculty help assistant instructors build skills that last well beyond a single course.

References

Teaching Tip: What Are You Really Trying to Assess?

As you design quizzes, projects, and exams, it’s worth pausing to ask: What am I really trying to assess? Too often, assessments measure peripheral skills like memorization, rather than the intended learning outcomes. For example, a timed coding exam may end up evaluating typing speed and syntax recall more than algorithmic thinking or problem-solving strategy. Similarly, a multiple-choice exam on HCI principles may privilege memorization over the ability to apply design heuristics to new contexts.

Evidence-based practices to align assessments with your goals:

  1. Backwards Design (Wiggins & McTighe, 2005)

  2. Constructive Alignment (Biggs, 1996)

    • Ensure that learning activities, assessments, and outcomes are in sync. For instance, if collaboration is a stated goal, include a group design critique, not just individual tests.

    • Example: Reflections on applying constructive alignment with formative feedback for teaching introductory programming and software architecture (2016): https://dl-acm-org.proxyiub.uits.iu.edu/doi/pdf/10.1145/2889160.2889185

  3. Authentic Assessment (Herrington & Herrington, 2007; )

  4. Reduce Construct-Irrelevant Barriers

    • If the skill being assessed is debugging, for example, provide starter code so students aren’t penalized for setup. If the goal is conceptual understanding, consider allowing open-book resources so recall doesn’t overshadow reasoning.

Students also struggle not because the concepts are beyond their ability, but because the expectations of the assessment are unclear.

For example:

  • A programming assignment asks students to “optimize” code, but it’s unclear whether grading is based on correctness, runtime efficiency, readability, or documentation.

  • A human–computer interaction (HCI) project requires a prototype, but is the emphasis on creativity, usability testing, or fidelity of the mockup?

  • An informatics paper asks for “analysis,” but it’s unclear whether success depends on critical thinking, proper use of data, or following citation conventions.

When assessments lack clarity, students must guess what matters. This shifts the focus from demonstrating learning to playing a hidden “what does the professor want?” game.

Why It Matters (Evidence-Based):

  • Cognitive Load: Ambiguous assessments create unnecessary cognitive load—students waste energy interpreting instructions instead of applying knowledge (Sweller, 2011).

  • Equity Impact: Lack of clarity disproportionately disadvantages first-generation and other structurally disadvantaged students, who may not have tacit knowledge about faculty expectations (Winkelmes et al., 2016).

  • Misalignment: As mentioned above, vague assessments often misalign with course outcomes, undermining constructive alignment (Biggs, 1996).

What Faculty Can Do:

  1. State the Core Construct: Ask yourself: Am I assessing correctness, creativity, reasoning, or communication? Then state it explicitly.

  2. Communicate Priorities: If multiple criteria matter, indicate their relative weight (e.g., correctness 50%, efficiency 30%, documentation 20%).

  3. Provide a Sample Response: A brief example—annotated to show what “counts”—helps students see what you value.

  4. Check for Hidden Criteria: If you penalize for style, clarity, or teamwork, ensure that’s written down. Otherwise, students perceive grading as arbitrary.

Faculty Reflection Prompt:
Pick one upcoming assignment and ask yourself: If I gave this to a colleague in my field, would they immediately know what I was assessing? Or would they have to guess? If the latter, refine the task or rubric until the answer is obvious.

Takeaway: Unclear assessments don’t just frustrate students, they distort what is being measured. By clarifying exactly what skill or knowledge is under the microscope, faculty ensure assessments are fair, transparent, and aligned with learning outcomes. Before finalizing any assignment or test, ask yourself: Am I measuring the skill that truly matters, or something adjacent? That small moment of reflection can make assessments more equitable, meaningful, and aligned with the professional practices of your discipline.

Quick Tip: Name the Thinking (Cognitive Skill), Not Just the Task

Name the Thinking (Cognitive Skill), Not Just the Task

When introducing a problem set, coding lab, or design activity, take 1–2 minutes to make the thinking process explicit. For example:

  • Instead of just saying: “Debug this code”
    Add: “This task is about identifying assumptions in how the code should work versus how it runs. Pay attention to the strategies you use: reading error messages, testing small chunks, or tracing variables.”

  • Instead of just saying: “Sketch a wireframe”
    Add: “This is about perspective-taking; imagining the interface from a novice user’s point of view.”

By naming the cognitive skill (debugging, pattern recognition, abstraction, empathy, systems thinking), students begin to see how their work maps onto the broader competencies of your field.

Why it matters:

  • Supports metacognition (students reflect on how they learn, not just what they learn).

  • Helps novice learners connect class tasks to professional practices.

  • Reinforces disciplinary literacies and makes hidden expectations visi