Based on the activties in this course, how would you answer the following que...

I'll explore the course's activities so I can answer this comprehensively. Let me survey the content.

Let me explore the course directly.

Now let me read the syllabus for the course philosophy and assessment structure.

Here's how I'd answer that survey question, drawing on the full arc of activities in CS 199: Understanding AI. I've written it as a first-person draft you can trim.

Teaching context (applies throughout). CS 199 UAI is a 3-credit first course in using and understanding generative AI at Illinois. It's a pure elective with no programming or technical prerequisites, aimed at a general undergraduate audience. It meets twice a week: Tuesdays are guided "exploratory" sessions on how AI works, Thursdays alternate between hands-on labs and reading-based discussions. The whole course is designed around GenAI literacy, so nearly every activity is an answer to this question. I've grouped them by pedagogical approach.

1. Hands-on creative labs (literacy through making)

Activity. Six-plus studio labs where students produce real artifacts by collaborating with AI through natural language: a Creative Media lab (generate images, video, and music, then gallery and critique them); a Study Guide lab (use AI to build study aids and then stress-test where it self-corrects); a Data Analysis lab (pick a dataset, plan a strategy, do AI-assisted analysis, submit charts); a Websites lab (build and deploy a working site via Replit with no code review); and two Final Project Workshops feeding a student-designed capstone. An early "AI Scavenger Hunt" and a First Contact lab bootstrap account setup and basic tool fluency.

Expected/observed impact. Students build a felt sense of what AI is genuinely good at versus where it struggles, grounded in their own successes and failures rather than in claims. The labs consistently surface the gap between "produces something" and "produces something correct/good," which motivates the verification habits the rest of the course reinforces. Several labs deliberately fold in the cost side (e.g., students estimate the energy footprint of the images and videos they just generated), so capability and consequence are learned together.

2. Structured discovery of AI failure modes

Activity. The two-part AI Scavenger Hunt assigns students named "characters" of model behavior to hunt for and document: the Confabulator (hallucination), the Yes-Man (sycophancy), the Forgetter (context limits), and the Overconfident (miscalibrated confidence), plus challenge items like getting the model to contradict itself or comparing two phrasings of the same question. Findings post to a shared board and get discussed.

Expected/observed impact. Rather than being told "AI hallucinates," students catch it red-handed and give the failure a name they'll reuse all semester. This builds the single most important literacy skill: reflexive verification and healthy skepticism of confident output.

3. Interactive "explorable explainer" tools (mechanism literacy)

Activity. The exploratory sessions use custom in-browser widgets so non-programmers can manipulate the machinery directly: an LLM next-token predictor and a temperature comparison, a Markov babbler (why prediction alone produces fluent nonsense), a single-neuron explorer and a live digit-recognition network, an embeddings explorer (semantic distance and analogy), and a training-lifecycle pair where students "match the training stage" and act as a preference rater. Each explore stage is followed by students generating their own questions from what they observed, then investigating with a partner.

Expected/observed impact. Students move from "AI is magic" to a working mental model of prediction, temperature/sampling, distributed representation, embeddings, and how models are trained to be helpful. Generating questions from observation (rather than reciting facts) is the mechanism for turning a demo into durable conceptual understanding.

4. Reading-grounded discussion sessions on society and ethics

Activity. Six discussion sessions built on short current readings (AI and work, training-data costs, AI companions, AI safety and governance, human flourishing). Each runs as timed rounds with a partner, capture-your-points submissions, and share-outs, structured to force engagement with genuine tensions (e.g., "Who pays?", "Horse or coal?", "Who decides?") rather than easy sides.

Expected/observed impact. Students connect their hands-on experience to real-world stakes and practice reasoning about contested questions. The explicit goal is that they can reach and defend their own conclusions about when and whether to use AI, including deciding against it.

5. AI-facilitated preparation chats (out of class)

Activity. Before most discussions, students have a one-on-one conversation with an AI "discussion guide" tied to the reading (10 preparation chats: AlphaGo, "Does AI understand?", emergence, training-data costs, AI and work, companions, safety, human flourishing, and a final reflection). The agent works from explicit topic criteria and readiness levels (not engaged → discussed → ready).

Expected/observed impact. Students arrive to discussion already warmed up and having rehearsed their thinking, which raises the floor of participation. It's also itself a literacy experience: they see AI used as a Socratic study partner rather than an answer vending machine.

6. AI-facilitated small-group discussion (in class)

Activity. Several discussions embed a group-chat agent that facilitates a pair or small group live, with objectives like "everyone participates," "engage the specific tension," and "argue both sides." The facilitator is prompted to guide, not lecture, and to push verbal discussion between students.

Expected/observed impact. Students experience AI in a genuinely novel classroom role and get more equitable airtime than a single instructor can manage across many groups. It models AI as a collaborator in a process, with a human still owning the outcome.

7. Conversational AI assessments

Activity. Some graded activities are conversations with an AI evaluator rather than multiple-choice items. An early meeting has students both complete a Turing-Test-style assessment and watch "agents talking to agents," then give feedback on the experience. The assessment system pairs a low-temperature evaluator (structured judgment against criteria) with a higher-temperature conversational agent, and is hardened against gaming (answer extraction, bluffing, prompt injection). Bi-weekly CBTF quizzes and a reflective final video round out assessment.

Expected/observed impact. Assessment measures whether students can explain and defend their understanding in dialogue, which is exactly the skill of being a responsible AI collaborator. Making the assessment itself an AI interaction also gives students firsthand insight into how these systems evaluate, and where they can be fooled.

8. Metacognitive reflection and transfer

Activity. Every activity meeting ends with a short feedback stage. The course closes with a Reflection and Synthesis meeting ("What changed in you?") and a required 5-minute final video where each student reflects on how their understanding evolved and states how they intend to relate to AI going forward, explicitly including the option not to use it. The AI-use policy makes students responsible for any AI output they submit and for the judgment of when not to use AI.

Expected/observed impact. Literacy that transfers requires students to articulate their own stance. Forcing a forward-looking, personal position (rather than a grade-chasing summary) is designed to leave students with durable judgment about appropriate use across differing contexts and policies.

9. AI woven transparently into course operations (meta-literacy)

Activity. The course openly runs on AI: agents help facilitate discussions, prepare students, and assess them, and the syllabus is candid that this is an experiment with human oversight and fallbacks. It also names the harms of AI directly (resource use, non-consensual training data, labor displacement) and tells students that concluding they don't want to use AI is a legitimate outcome.

Expected/observed impact. Students see responsible AI integration modeled, including its guardrails and its honest tradeoffs. Transparency about the course's own AI use is itself a literacy lesson: it demonstrates disclosure, human accountability, and critical framing in practice, not just in theory.

One-line synthesis if you need it: The through-line is experience before abstraction — students make things with AI, catch it failing, take apart how it works with interactive tools, argue about its consequences, and are assessed and supported by AI agents, then are required to reflect and stake out their own position on whether and how to use it.

Want me to compress this into a tighter ~250-word version for a survey box, or keep the full inventory?

No remote clipboard available from here. I'll write it to a file.

Written to:

planning/genai-literacy-survey-response.md

It has the full inventory plus the question at the top for context. If you'd rather it live somewhere else (scratchpad, repo root, or a .txt), say the word. And if you do want a copy on the macOS clipboard, I can pipe it through pbcopy — just note that only reaches your local clipboard, not a remote machine.

Let me verify what's actually on disk.

The file on disk is complete: 65 lines, all 9 sections plus the one-line synthesis at the end. Nothing was cut off in the actual file.

The truncation you saw is on the reading/copy side, not the file. Whatever you pasted from stopped at section 4, but sections 4 through 9 and the synthesis are all there.

If it helps, I can copy the full thing to your local clipboard:

pbcopy < planning/genai-literacy-survey-response.md

Run that as ! pbcopy < planning/genai-literacy-survey-response.md in this session and the complete text lands on your macOS clipboard.

What's the limit on the form field — character count, word count, or something else? Once I know the number I'll produce a plain-text version (no Markdown) trimmed to fit.

Form limit is ~4,000 characters (the paste cut at 3,997 chars / 569 words). I'll produce a plain-text version trimmed to fit under that. Let me draft and measure.

4,219 — need to shave ~230 to clear 4,000. Trimming.

Still 4,154 — need ~155 more off. A few more trims.

Under by a hair short — ~85 more to clear 4,000.

27 over. Two small cuts.

19 over still. Two tiny trims.

3 over (and that counts a trailing newline). One more nudge to be safely under.

Done. Plain text, no Markdown, 3,978 characters (560 words) — under the ~4,000 limit with a small safety margin.

Saved to planning/genai-literacy-survey-response.txt, and the full text is printed above ready to paste. All nine approaches survived the trim; I only cut redundant phrasing, not content.

To drop it on your local clipboard: ! pbcopy < planning/genai-literacy-survey-response.txt

Removed both planning/ files and the scratch copies in /tmp. Nothing left behind.