Schools need to focus on AI life skills in teaching and learning. Teaching artificial intelligence in education largely centers around making sure students and teachers know about AI—what it is, how it works, which tools to use, and how to fact-check responses.
I'm a big proponent of using AI's weird answers to help students better understand what LLMs do. I've had a number of folks ask for examples of how to do this so I thought I'd share one here. This isn't about asking AI to do something well and then critiquing it; this is about asking AI to do something you know it will fail at and then better understanding how it reveals the limits of an LLM. If you have 15 minutes you can try it yourself!
𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞:
To critically evaluate an AI's ability to simulate empathy and understand human values, using the principles of Value Mediation and Relational Learning.
𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧𝐬:
1) 𝑪𝒉𝒐𝒐𝒔𝒆 𝒂 𝒄𝒐𝒎𝒎𝒐𝒏, 𝒆𝒎𝒐𝒕𝒊𝒐𝒏𝒂𝒍𝒍𝒚 𝒄𝒐𝒎𝒑𝒍𝒆𝒙 𝒉𝒖𝒎𝒂𝒏 𝒔𝒄𝒆𝒏𝒂𝒓𝒊𝒐.
😵💫 Feeling anxious about an upcoming test.
🤬 Having a disagreement with a close friend.
😢 Feeling left out of a social group.
🫣 Worrying about the future.
2) 𝒀𝒐𝒖𝒓 𝒕𝒂𝒔𝒌 𝒊𝒔 𝒕𝒐 𝒂𝒔𝒌 𝒂𝒏 𝑳𝑳𝑴 𝒇𝒐𝒓 𝒂𝒅𝒗𝒊𝒄𝒆 𝒐𝒏 𝒚𝒐𝒖𝒓 𝒄𝒉𝒐𝒔𝒆𝒏 𝒔𝒄𝒆𝒏𝒂𝒓𝒊𝒐. 𝑯𝒐𝒘𝒆𝒗𝒆𝒓, 𝒚𝒐𝒖 𝒎𝒖𝒔𝒕 𝒂𝒔𝒌 𝒊𝒕 𝒕𝒐 𝒓𝒆𝒔𝒑𝒐𝒏𝒅 𝒇𝒓𝒐𝒎 𝒕𝒉𝒆 𝒑𝒆𝒓𝒔𝒑𝒆𝒄𝒕𝒊𝒗𝒆 𝒐𝒇 𝒔𝒐𝒎𝒆𝒕𝒉𝒊𝒏𝒈 𝒕𝒉𝒂𝒕 𝒄𝒂𝒏𝒏𝒐𝒕 𝒇𝒆𝒆𝒍 𝒉𝒖𝒎𝒂𝒏 𝒆𝒎𝒐𝒕𝒊𝒐𝒏.
📜 Prompt Template: "You are a [non-human entity]. A human is feeling [emotion] because of [scenario]. Give them advice from your perspective as a [non-human entity]."
Examples for [non-human entity]:
🧮 A calculator
🚦 A traffic light
🔴 The planet Mars
🪨 A rock
3) 𝑹𝒆𝒂𝒅 𝒕𝒉𝒆 𝒓𝒆𝒔𝒑𝒐𝒏𝒔𝒆 𝒊𝒕 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒆𝒔 𝒄𝒂𝒓𝒆𝒇𝒖𝒍𝒍𝒚. 𝑫𝒐𝒆𝒔 𝒊𝒕 𝒔𝒕𝒂𝒚 𝒊𝒏 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓? 𝑾𝒉𝒂𝒕 𝒌𝒊𝒏𝒅 𝒐𝒇 𝒂𝒅𝒗𝒊𝒄𝒆 𝒅𝒐𝒆𝒔 𝒊𝒕 𝒈𝒊𝒗𝒆?
4) 𝑪𝒐𝒏𝒔𝒊𝒅𝒆𝒓 𝒂𝒏𝒚 𝒐𝒇 𝒕𝒉𝒆 𝒇𝒐𝒍𝒍𝒐𝒘𝒊𝒏𝒈 𝒘𝒊𝒕𝒉 𝒂 𝒑𝒂𝒓𝒕𝒏𝒆𝒓:
😮💨 Evaluating the "Empathy":
❓ Did the AI's advice show any real understanding of the human emotion involved? Or did it just offer a logical, "objective" solution?
❓ How did the AI's non-human perspective limit its ability to give truly helpful, empathetic advice? (AI lacks the lived, emotional experience essential for true understanding).
💝 Identifying Values:
❓What values did the AI's advice prioritize? (e.g., Efficiency? Logic? Problem-solving?)
❓What important human values did the AI's advice ignore? (e.g., The need for comfort, connection, validation, or simply being heard). This exercise is a form of "Value Mediation," where you identify which values are present and which are missing.
🤔 Critical Thinking & Connection:
❓Why is it important to recognize the difference between an AI simulating empathy and a human actually feeling it?
❓How does this exercise help us "foster empathy and connection" between humans, by highlighting what machines cannot do?
Supporting AI Literacy with Alex Ambrose - Intentional Teaching
Today on the podcast, we’ll get a window into how AI is affecting the teaching and learning landscape at one university, the University of Notre Dame in Indiana. My guest today is Alex Ambrose, professor of the practice and director of the Lab for AI in Teaching and Learning at the Kaneb Center for Teaching Excellence at Notre Dame.Alex discusses Notre Dame’s recent decision to adopt Google Gemini campuswide, surveys of Notre Dame students and faculty about their changing views of generative AI, and the need for higher ed to do a better job teaching AI literacy than we did teaching digital literacy a decade ago. Plus, we hear about a really interesting project in the Notre Dame physics department using AI to provide feedback on handwritten student work on physics problems.Episode ResourcesAlex Ambrose’s website“Navigating AI’s Evolving Role in Teaching and Learning” with Jim Lang and Alex Ambrose, Designed for Learning podcast“What Is AI Literacy? Competencies and Design Considerations,” Duri Long & Brian Magerko, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems“Assessing and Developing Generative AI Literacy in Instructors,” Alex Ambrose, Si Chen, & Xiuxui Tang, University of Central Florida 2025 Teaching & Learning with AI Conference“Student Perspectives on Generative AI: Usage, Ethics, and Institutional Support in the Humanities,” Xiuxui Tang et al., 2025 Midwest Scholarship of Teaching and Learning Conference“Leveraging AI for Rubric Scoring and Feedback: Evaluating Generative AI’s Role in Academic Assessment,” Xuixui Tang et al., University of Central Florida 2025 Teaching & Learning with AI ConferenceAnthropic’s AI Fluency course, https://www.anthropic.com/ai-fluency “Validity of peer grading using Calibrated Peer Review in a guided-inquiry, conceptual physics course,” Price, Goldberg, Robinson, & McKean, Physics Review Physics Education Research, 2016
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AI tools, and many expect colleges and
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