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Exploring Teaching and Learning in Luddy Indiana University

Join us for a Faculty Lunch & Learn: Teaching with AI

Posted on by akesha

Dear colleagues,
You’re invited to an informal Faculty Lunch & Learn; a chance to connect, share ideas about teaching with AI, and help shape future faculty conversations.
When: Friday, November 7th, 2025
Where: BLLU 2005 – (Luddy AI Center)
Lunch/Refreshments: Provided
This is a relaxed, conversational gathering, no slides, no presentations, just good company and meaningful discussion.
We’ll explore questions like:
  • How are faculty and students using AI tools in learning?
  • What new teaching approaches are emerging?
  • What challenges or opportunities are you seeing?
Please RSVP https://events.iu.edu/event/2103620-faculty-lunch-learn-teaching-with-ai by Thursday, November 6th at noon, so we can plan for lunch. 

Free Webinars for Universal Design for Learning This Week

Posted on by akesha

9-in-9 Overtime: UDL 3.0 Insights

Free Webinars Where Universal Design for Learning Expands Beyond Boundaries

Ready to dive deeper into UDL 3.0? Our groundbreaking “9-in-9” series has sparked such enthusiasm that we’re amplifying its impact! Due to overwhelming responses and rich proposal submissions, we’re expanding our horizons to bring you even more transformative insights into Universal Design for Learning 3.0. Each 60-minute webinar delivers focused, actionable strategies specifically designed for higher education. You’ll gain practical tools and resources readily adaptable to any discipline. Visit the 9-in-9 Overtime Padlet for a complete listing of webinars.

Register now to secure your spot in this enhanced professional development journey! You may register for the remaining webinars HERE.

October 29, 2025, 12:00 pm – 1:00 pm Eastern — Sprinkles of Surprise and Delight: Bringing Joy and Play into Higher Education

Danny Smith will share insights and actionable strategies for applying the UDL 3.0 Guideline, Welcoming Interests and Identities (7): Nurture Joy and Plan (7.3), across modalities and disciplines.

Select HERE to register for this free virtual webinar.

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

Posted on by akesha

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 OpenAIillustrates 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, and clarification on last week’s post.

Posted on by akesha

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.

 

 

CLARIFICATION – I want to provide a clarification to the earlier blog post on handling attendance concerns. In the previous version, I suggested that faculty direct students who report short-term illnesses to the Student Care and Resource Center https://studentlife.indiana.edu/care-advocacy/care-and-resource-center/index.html  or Accessible Educational Services https://studentlife.indiana.edu/care-advocacy/iub-aes/index.html. Upon review, that guidance was too broad. Those offices should only be contacted when a student’s absence is extended, ongoing, or connected to a documented accommodation or serious circumstance.

 

For routine short-term absences (such as brief illnesses or personal emergencies), faculty should handle communication and make-up arrangements directly with the student using their established course policy without requiring documentation. This adjustment aligns more closely with institutional policy and helps prevent overwhelming support offices while maintaining fairness and flexibility for students. I have updated this blog post https://blogs.iu.edu/luddyteach/2025/10/09/navigating-attendance-challenges-in-the-modern-classroom-practical-tips-for-faculty/ in red with suggested language for your syllabi. Additionally, I have provided suggested policies you can adapt at the end of the blog post for missed exams in large classes.

 

UPDATED: Navigating Attendance Challenges in the Modern Classroom: Practical Tips for Faculty

Posted on by akesha

Hi All,

I have received a few inquiries related to addressing attendance concerns arising in courses. I have compiled my responses in hope they can help you. Please let me know if additional feedback or clarification is needed.

Managing attendance has always been a balancing act; but in today’s teaching landscape, it’s more like walking a tightrope. Between health-related absences, caregiving responsibilities, flexible attendance accommodations, and student disengagement, it can feel increasingly difficult to find the right mix of empathy, fairness, and structure.

Many of our colleagues are asking the same questions:

  • What should I do when a student emails right before the exam saying they’re sick?
  • How do I handle students who “forget” to show up?
  • How do I support students who miss multiple classes but still seem interested in finishing strong?

Below are practical, policy-aligned tips and strategies that can help faculty navigate these common, but complex, attendance scenarios.

  1. Move from Policing to Partnership

Faculty cannot (and should not) request or require doctor’s notes or other forms of medical documentation. The IU Student Health Center no longer provides verification forms, and asking for medical proof raises equity and privacy concerns.

Instead, redirect students to official university support channels:

Tip: Keep a short paragraph in your syllabus or email template that says:

“If health or personal challenges interfere with your coursework, please contact the Student Care and Resource Center or Accessible Educational Services. They can help coordinate official support so we can make appropriate adjustments.”

This shifts responsibility to the student while maintaining your compassion and professional boundaries.

 

  1. Redefine Attendance as Engagement

“Flexible attendance” is not new; it’s just newly emphasized. Post-pandemic teaching asks us to think less about butts in seats and more about meaningful participation.

 

Consider measuring engagement instead of simple presence. This could include:

 

  • Submitting reflection prompts or “exit tickets” after class
  • Participating in online discussion threads or collaborative documents
  • Contributing to group projects or peer reviews
  • Completing brief “pre-class” or “after-class” questions in Canvas

 

These approaches give students multiple pathways to stay involved, even when they must miss class for legitimate reasons. The CITL guide on Attendance and Student Engagement https://citl.indiana.edu/teaching-resources/teaching-strategies/attendance-engage/index.html offers more ideas and sample syllabus language.

 

  1. Responding to Common Scenarios

When a student emails right before the exam claiming illness:
Acknowledge with care and redirect appropriately.

“Thank you for letting me know. I hope you’re feeling better soon. If your illness continues or affects multiple classes, please reach out to the Student Care and Resource Center for documentation so I can plan your make-up exam.” (If this aligns with the policy stated in your syllabus.

Acknowledge with care, preserve privacy, and apply your course policy consistently.

“Thank you for letting me know. I hope you’re feeling better soon. Please take the time you need to recover. Once you’re able, check the course site for next steps or reach out so we can discuss options for making up missed work, consistent with the course policy.” Scroll to the bottom for suggested course policies….

Key points:

  • Faculty should not require documentation or ask for medical details.
  • If the absence appears to be extended or recurring, then it’s appropriate to suggest the student contact the Student Care and Resource Center or Accessible Educational Services for additional support.
  • For routine short-term absences (e.g., a cold, brief illness), it’s best handled directly between instructor and student using the course’s stated make-up policy.

This language keeps responsibility with the student, avoids unnecessary referrals, and aligns with IU’s intent: minimize administrative barriers while supporting legitimate needs.

I will note that this approach protects privacy, avoids the “prove it” dynamic, and keeps you aligned with IU policy.

  • Don’t ask for doctor’s notes
  • Refer to official channels (SCRC or AES)
  • Apply your make-up policy consistently

When a student simply forgets the exam:
It’s reasonable to follow your stated policy and not offer a make-up. A kind but firm response works best:

“I understand mistakes happen, but make-up exams are reserved for documented emergencies. Unfortunately, forgetting the exam time doesn’t qualify for a make-up opportunity.”

You may choose to offer a limited alternative if it’s a first-time lapse from an otherwise reliable student—but be consistent to avoid fairness concerns.

 

When a student misses multiple assessments but is still partially engaged:
Encourage re-engagement instead of resignation. Try:

  1. Flag the student via the Student Engagement Roster (SER) so advisors can step in.
  2. Send a check-in message: “I’ve noticed you’ve missed a few assessments. How are things going? Let’s talk about how to get back on track.”
  3. Offer a structured path forward:
    • Allow a make-up exam or replacement activity (if your syllabus allows)
    • Substitute a reflective learning journal for missed low-stakes quizzes
    • Set clear expectations for the remainder of the semester

If recovery seems unlikely, refer the student to academic advising to discuss withdrawal before deadlines. Please note: The advisors have a holistic snapshot of the student’s experience and can better gauge to how a withdrawal will impact their overall academic career and financial aid.

 

  1. Balancing Fairness and Flexibility

Fairness does not mean treating every student identically—it means providing equitable opportunities to succeed. Transparency is key. Clearly explain your attendance and make-up policies in the syllabus, including the rationale behind them.

For example:

“Attendance and engagement are key to mastering course material. This policy is designed to encourage consistent participation while allowing flexibility for legitimate health or personal issues.”

By articulating your intent, you reduce misunderstandings and help students see your policies as supportive, not punitive.

 

  1. Practical Prevention Strategies

Beyond policies and emails, a few small teaching habits can dramatically reduce attendance issues:

  • Set clear expectations early. Review attendance policies aloud during the first week.
  • Communicate the value of class time. Explain what students miss when they’re not present—like peer learning, examples, or practice time.
  • Make learning visible. Use exit tickets or one-minute reflections to show how participation connects to mastery.
  • Interactive Lectures: Consider providing students with formative assessments throughout your lecture (low stake questions answered through TopHat or Canvas) or engaging in other activities that connect with the themes of the lecture.
  • Reach out early. If you notice a pattern of absences, a simple “I missed you in class; is everything okay?” Email can prevent disengagement from snowballing.
  • Model reliability. Start and end class on time, show empathy without overextension, and follow your own policies consistently.

 

  1. Where Faculty Can Get Help

When in doubt, you don’t have to navigate attendance challenges alone.

Resources:

  • ME!  FYI – My office is now called Academic Learning and Engagement. While I am still located in 2138 I am happy to meet with you online or face-to-face to strategize concerns specific to your context.
  • Center for Innovative Teaching & Learning (CITL): Guidance on course design and syllabus language. (teaching@indiana.edu)
  • Student Care and Resource Center (SCRC): Help interpreting attendance memos and coordinating student support.
  • Accessible Educational Services (AES): For accommodation-related questions. (aes@indiana.edu)
  • Student Engagement Roster (SER) https://ser.indiana.edu/faculty/index.html : Early alert system for advisors and support staff. Matthew Broussard, Director of Undergraduate Advising here in Luddy noted: “We are happy to help them navigate any needed enrollment changes and consequences that may have. We can also discuss general success practices if they are missing class/assignments.  With SER flags, details are wonderful! If the recommendation is that a student withdraw and that is written in the SER flag, that helps us know how to steer our conversation with them. Or if they can stay in the course but need a certain threshold to pass and that’s in the SER flag,  we can help discuss strategies with that student to get there.”
  • Teaching Community of Practice: Connect with colleagues via our Luddy Teaching and Learning group meetings which can share real-world solutions for managing large-enrollment courses. We have a planning meeting on Tuesday and will announce our meeting dates later next week. All faculty and teaching staff are welcome to participate.

Final Thought: Compassion with Boundaries

Attendance challenges often reveal deeper student struggles: illness, burnout, financial pressure, or lack of confidence. Balancing compassion with boundaries protects both student learning and faculty well-being. The goal is not to eliminate every absence, but to build a culture of engagement and accountability that helps students succeed without burning instructors out in the process.

–

Here’s a sample copy-paste-ready policy you can use (and adapt) for missed exams in large classes. It protects student privacy (no doctor’s notes), avoids overwhelming support offices, reduces policing, and stays equitable for students who do show up.

Missed Exam & Short-Term Illness Policy (No-Documentation)

Principles

  • We prioritize learning, privacy, and fairness.
  • Short-term illness does not require medical documentation.
  • Make-ups are structured, time-bound, and consistent to remain fair to everyone.

Possible policy

1) Eligibility & Notification

  • Short-term illness or emergency: If you are ill or experience an acute issue, email me as soon as you can (ideally before the exam start). No medical details needed.
  • One no-questions-asked token: Each student has one (1) “illness/emergency token” per term for a major assessment (exam, midterm). Use of the token = eligibility for a make-up (see timelines below).
  • Non-emergency conflicts (work, travel, interviews): Not covered by this policy; request in advance per syllabus guidelines.

2) What’s not covered

  • Ongoing or repeated absences (2+ major assessments affected) signal a broader need; students should meet with their academic advisor to discuss options (including withdrawal timelines). Only then, if appropriate, we’ll coordinate next steps. (This avoids overwhelming Student Care/AES for routine short-term issues.)

3) Timelines & Process for Make-Ups

  • Request window: Student must email within 24 hours of the missed exam (or earlier if possible).
  • Make-up window: The make-up must be completed within 5 business days of the original exam unless I specify a common make-up date for the course.
  • Format: Make-ups may be a different but equivalent version, oral verification, or a proctored alternate; academic integrity rules fully apply.
  • Limit: Only one major assessment may be made up via the token. Additional missed majors assignments  require an advisor conversation; further make-ups not guaranteed.

4) Low-Stakes Work (Quizzes, Clickers, Exit Tickets)

To reduce stress and policing for short absences:

  • Drop policy: We drop the lowest 2 quizzes (or X% of low-stakes items).
  • No retroactive make-ups for low-stakes items beyond that drop.
  • Optional alternative (pick one): allow one “quiz bundle” reflection (short write-up on missed topics) to recover up to 50% of the points for up to 2 missed quizzes.

5) Equity & Transparency

  • The policy is identical for all students; discretion (e.g., first-time “forgot” accommodation) is used sparingly and consistently.
  • No medical documentation or details will be requested.
  • Patterns suggesting extended challenges → prompt referral to academic advising first (they coordinate resources and timelines).

6) Communication Templates (use in email/Canvas)

If a student reports illness right before the exam

Thanks for letting me know. Please focus on your health. You may use your no-questions-asked make-up token for this exam. Reply within 24 hours to confirm. The make-up will occur by [date/time window]; details to follow. No medical documentation is required.

If absences are recurring

I’m noticing multiple assessments affected. At this point, the best next step is to meet with your academic advisor to review options and deadlines. After that conversation, email me so we can determine what’s feasible going forward.

7) Large-Class Logistics (to save your time)

  • Use a Canvas “Self-Attestation Quiz” (1–2 questions: “I’m using my one token,” “I understand the 5-day window”). Due within 24 hours; this auto-records requests.
  • Publish one common make-up slot (with overflow slot) rather than case-by-case meetings. If you have TA support you might schedule more slots to accomodate for more students (in a large enrollment course).
  • Create an alternate exam bank in advance; include a brief oral verification for borderline cases.
  • Post a one-page flowchart in Canvas (Missed exam? → Token? → Deadline? → Next steps).

 

Why this is fair

  • Students who show up aren’t disadvantaged: limits, timelines, and equivalent rigor keep grading credible.
  • Students with legitimate short-term issues get a humane on-ramp without paperwork barriers.
  • You avoid overload: no doctor’s notes, minimal one-off negotiations, and fewer referrals that swamp campus offices.
  • Clarity reduces conflict: expectations and scripts are public and consistent.

Optional knobs you can set (pick and standardize)

  • Make-up window: 3 vs. 5 business days.
  • “Forgot” consequence: zero vs. one-time 50% capped alternative.
  • Low-stakes policy: 2 drops vs. 1 drop + 1 reflection recovery.

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

Posted on by akesha

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

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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

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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?

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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.

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

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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 visible.
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