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!

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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.

Pedagogical Tips for the Start of the Semester

The first weeks of the semester are a unique window to shape not only what students will learn, but how they will learn. In STEM courses, where concepts can be abstract, skill levels vary wildly, and technologies evolve quickly, intentional, evidence-based practices can help you set students up for long-term success.

Below are a few strategies with examples and tools you can implement immediately.

Design an Inclusive, Transparent Syllabus

Evidence base: Transparent teaching research (Winkelmes et al., 2016) shows that when students understand the purpose, tasks, and criteria for success, they perform better.

Implementation tips:

  • Purpose statements: For every major assignment, include a short note on why it matters and how it connects to industry or future coursework.
    Example: “This database schema project builds skills in relational modeling, which are directly relevant to backend software engineering interviews.”

  • Clear expectations: Break down grading policies, late work policies, and collaboration guidelines into plain language, avoiding overly technical or legalistic phrasing.

  • Accessibility & flexibility: Link to tutoring labs, office hours, online learning resources, and note-taking tools. Indicate whether assignments can be resubmitted after feedback.

  • Create a one-page “Quick Reference” sheet covering key policies (late work, collaboration, grading)

  • Norm-setting: Add a “Community Norms” section that covers respectful code reviews, how to ask questions in class, and expectations for group work. In large classes, it’s vital to set expectations for respectful online discussions, effective use of the Q&A forum (e.g., checking if a question has already been asked), and guidelines for group work if applicable (e.g., conflict resolution strategies).

Establish Psychological Safety Early

Evidence base: Google’s Project Aristotle (2015) and Edmondson’s (1999) work on team learning show that psychological safety, where students feel safe to take intellectual risks, is essential for high performance.

Implementation tips:

  • Low stakes start: In week one, run short, open-ended coding challenges that allow multiple solutions. Make it clear that mistakes are part of the process.

  • Start with anonymous polls about programming experience to acknowledge the diversity of backgrounds in the room.

  • Instructor vulnerability: Share a personal example of a bug or failed project you learned from. This normalizes challenges in programming. In a large lecture, you can briefly mention common misconceptions students often have with a new concept, and how to navigate them.

  • Model Constructive Feedback: When providing feedback on early assignments (even low-stakes ones), focus on growth and learning. When addressing common errors in a large class, frame it as an opportunity for collective learning rather than pointing out individual mistakes.

  • Multiple communication channels: Set up a Q&A platform (InScribe) where students can post questions anonymously.

Use Early Analytics for Intervention

Evidence base: Freeman et al. (2014) found that early course engagement strongly predicts later success, allowing for timely support.

Implementation tips:

  • Student Engagement Roster (SER): https://ser.indiana.edu/faculty/index.html During the first week of class,  consider explaining the SER to your students and tell them how you will be using it. If students are registered for your class and miss the first class, report them as non-attending in SER.  It will allow outreach that can help clarify their situation. Here’s a sample text you could put into your syllabus:
    This semester I will be using IU’s Student Engagement Roster to provide feedback on your performance in this course. Periodically throughout the semester, I will be entering information on factors such as your class attendance, participation, and success with coursework, among other things. This information will provide feedback on how you are doing in the course and offer you suggestions on how you might be able to improve your performance.  You will be able to access this information by going to One.IU.edu and searching for the Student Engagement Roster (Faculty) tile.

  • Use Canvas Analytics:

  • Identify struggling students. “Submissions” allows you to view if students submit assignments on-time, late, or not at all.

    1. See grades at a glance. “Grades” uses a box and whisker plot to show the distribution of grades in the course.

    2. See individual student data. “Student Analytics” shows page view, participations, assignments, and current score for every student in the course.

  • Track early submissions: Note which students complete the first assignments or attend early labs

  • Personal outreach: Email or meet with students who are slipping to connect them with tutoring, peer mentors, or study groups.

  • Positive nudges: Celebrate early wins (e.g., “I noticed you submitted the optional challenge problem. Great initiative!”).

  • Proactive Outreach (with TA Support): If you identify students who are struggling, send personalized emails offering support and directing them to available resources (e.g., tutoring, office hours with TAs). Consider delegating some of this outreach to TAs in large courses.

  • Announcements Highlighting Resources: Regularly remind the entire class about available support resources, study strategies, and upcoming deadlines through announcements.

Key Implementation Strategies for Success

  • Start Small and Build Don’t attempt to implement all strategies simultaneously. Choose 2-3 that align with your teaching style and course structure, then gradually incorporate additional elements.

  • Leverage Your Teaching Team In large courses, TAs are essential partners. Invest time in training them on consistent feedback practices, student support strategies, and early intervention protocols.

  • Iterate Based on Data Use student feedback, performance analytics, and your own observations to refine your approach throughout the semester. What works in one context may need adjustment in another.

  • Maintain Connection at Scale Even in large courses, students need to feel seen and supported. Use technology strategically to maintain personal connection while managing the practical demands of scale.

Conclusion

By implementing these research-backed strategies, faculty can create learning environments where diverse students thrive, engagement remains high, and learning outcomes improve significantly.

The investment in implementing these practices pays dividends not only in student success but also in teaching satisfaction and course sustainability. As you prepare for the new semester, consider which strategies best align with your course goals and student population, then take the first step toward transforming your large enrollment course into a dynamic, supportive learning community.

Remember: even small changes, consistently applied, can create significant improvements in student learning and engagement. Start where you are, use what you have, and do what you can to create the best possible learning experience for your students.

References

  1. Winkelmes, M. A., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K. H. (2016). A teaching intervention that increases underserved college students’ success. Peer Review, 18(1/2), 31–36. Association of American Colleges and Universities.

  2. Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999

  3. Google Inc. (2015). Project Aristotle: Understanding team effectiveness. Retrieved from https://rework.withgoogle.com/intl/en/guides/understanding-team-effectiveness

  4. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111

Supporting non-majors in introductory computer courses

The article titled “Exploring Relations between Programming Learning Trajectories and Students’ Majors” https://dl.acm.org/doi/fullHtml/10.1145/3674399.3674497 investigates how students from various academic disciplines learn programming in a compulsory introductory programming course consisting of 75 students, with 40 majoring in CS and 35 in non-CS majors. “They were all freshmen without prior programming experience. Considering their similar scores of entrance exam to this university, it can be assumed that their levels of mathematical logic and computational thinking were roughly comparable”.

The authors note that “an increasing number of non-computer science students are now engaging in programming learning. However, they often struggle in early programming courses. The researchers analyzed data from students’ learning processes to understand how their major influences their learning journey in programming.

The study found that students’ backgrounds and areas of study can affect how they approach and progress in learning programming. They suggest:

  1. Making Programming Relevant: When teaching programming to students who aren’t majoring in computer science, it’s important to connect the lessons to things that are important to them. For example, showing how programming can be used in art, music, or business can make the subject more interesting, especially at the start of the course.

  2. Paying Extra Attention to Struggling Students: Teachers should keep a close eye on students who are not doing well or aren’t very interested in the course. These students might need extra help to keep up, so they don’t fall behind. Connecting them with teaching assistants, Luddy tutors, and additional resources early in the semester could be helpful.

  3. Using Tests to Track Progress: For computer science students, instructors can use quizzes and smaller tests throughout the semester to see how well they’re doing. This helps faculty know if they’re learning. However, for non-CS students who are doing well, these smaller tests might not show their full abilities, so the teacher needs to be extra careful when evaluating their skills. These students might be good at memorizing facts or completing basic tasks in the test, but that doesn’t mean they fully grasp the deeper concepts or could apply the knowledge in real-world situations. So, instructors need to be extra thoughtful and consider other ways to evaluate their skills, not just based on these smaller tests.

Example:

Imagine a student in a business major who is acing the quizzes in a programming class. They might be good at solving problems that are simple and similar to what they’ve studied, but the quizzes might not show how well they can use programming to solve real business problems. Faculty might need to look at other work, like projects or group activities, to better understand the student’s true abilities.

Implications for Teaching and Learning:

  • Tailored Instruction: Educators can design programming courses that consider the diverse backgrounds of students, offering different learning paths or support based on their major.Example: In a programming class, students from a data science major might already have some knowledge of coding, so the instructor could offer them more advanced challenges while giving students from a humanities background more basic programming tasks. This ensures that all students are working at a level that matches their prior knowledge, making learning more effective.

  • Early Support: Providing additional resources or guidance early in the course can help students who might struggle due to their major’s focus, ensuring they keep up with the material.Example: In the first few weeks of a programming course, an instructor might offer extra study sessions or online tutorials for students from non-technical majors (like business or social sciences). These students may find programming challenging, so additional support would help them catch up and build their confidence early in the course.

  • Encouraging Diverse Majors: Encouraging students from various disciplines to engage with programming can enrich their learning experience and broaden their skill set.Example: A university might organize workshops to show students from creative fields (like art or design) how programming can help them bring their ideas to life, such as creating interactive websites or digital art. Encouraging students from these fields to explore programming opens new possibilities for their careers and learning.

By understanding the relationship between a student’s major and their programming learning trajectory, educators can create more effective and supportive learning environments.