# Learning to Prompt in the Classroom to Understand AI Limits: A pilot study

arXiv: https://arxiv.org/abs/2307.01540 · HTML: https://mlsanity.com/paper/2307.01540/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [On the Paradox of Learning to Reason from Data](https://mlsanity.com/paper/2205.11502/) (2022)
- [On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)](https://mlsanity.com/paper/2302.06706/) (2023)
- [Exploring the Universal Vulnerability of Prompt-based Learning Paradigm](https://mlsanity.com/paper/2204.05239/) (2022)
- [Conceptual Metaphors Impact Perceptions of Human-AI Collaboration](https://mlsanity.com/paper/2008.02311/) (2020)
- [Beyond the Safeguards: Exploring the Security Risks of ChatGPT](https://mlsanity.com/paper/2305.08005/) (2023)
- ["I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data](https://mlsanity.com/paper/2212.05856/) (2022)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- [Long-Term Trends in the Public Perception of Artificial Intelligence](https://mlsanity.com/paper/1609.04904/) (2016)
- [Artificial Intelligence is stupid and causal reasoning won't fix it](https://mlsanity.com/paper/2008.07371/) (2020)
- [Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix)](https://mlsanity.com/paper/2306.10645/) (2023)
- [Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion](https://mlsanity.com/paper/2102.04211/) (2021)
- [Cases of EFL Secondary Students' Prompt Engineering Pathways to Complete a Writing Task with ChatGPT](https://mlsanity.com/paper/2307.05493/) (2023)
- [The potential of artificial intelligence for achieving healthy and sustainable societies](https://mlsanity.com/paper/2202.07424/) (2022)

## Built on by (most cited first, 6 of 6)

- [Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines](https://mlsanity.com/paper/2504.07840/) (2025)
- [Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs](https://mlsanity.com/paper/2411.19554/) (2024)
- [The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Education](https://mlsanity.com/paper/2501.16061/) (2025)
- [The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration](https://mlsanity.com/paper/2605.18372/) (2026)
- [S2LPP: Small-to-Large Prompt Prediction across LLMs](https://mlsanity.com/paper/2505.20097/) (2025)
- [AI Pedagogy: Dialogic Social Learning for Artificial Agents](https://mlsanity.com/paper/2507.21065/) (2025)

## Abstract (arXiv)

Artificial intelligence's (AI) progress holds great promise in tackling pressing societal concerns such as health and climate. Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems allowing them to process an unprecedented amount of unstructured data. However, the ensuing excitement has led to negative sentiments, even as AI methods demonstrate remarkable contributions (e.g. in health and genetics). A key factor contributing to this sentiment is the misleading perception that LLMs can effortlessly provide solutions across domains, ignoring their limitations such as hallucinations and reasoning constraints. Acknowledging AI fallibility is crucial to address the impact of dogmatic overconfidence in possibly erroneous suggestions generated by LLMs. At the same time, it can reduce fear and other negative attitudes toward AI. This necessitates comprehensive AI literacy interventions that educate the public about LLM constraints and effective usage techniques, i.e prompting strategies. With this aim, a pilot educational intervention was performed in a high school with 21 students. It involved presenting high-level concepts about intelligence, AI, and LLMs, followed by practical exercises involving ChatGPT in creating natural educational conversations and applying established prompting strategies. Encouraging preliminary results emerged, including high appreciation of the activity, improved interaction quality with the LLM, reduced negative AI sentiments, and a better grasp of limitations, specifically unreliability, limited understanding of commands leading to unsatisfactory responses, and limited presentation flexibility. Our aim is to explore AI acceptance factors and refine this approach for more controlled future studies.