mlsanityData to 7 Oct 2026

Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix)

arXiv 2306.10645 ↗First posted 18 Jun 2023cs.HC cs.AI cs.CYcited by 6

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Abstract (arXiv)

Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn chatbot interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue multiple interconnected learning objectives, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the limited history maintained for the conversation and the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 18 Jun 2023.

Builds on

15 of 53 references.

References · 15

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  3. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 628
  4. 2023Hyena Hierarchy: Towards Larger Convolutional Language Modelscited by 388
  5. 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 324
  6. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
  7. 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
  8. 2020Recipes for Safety in Open-domain Chatbotscited by 168
  9. 2023A Categorical Archive of ChatGPT Failurescited by 167
  10. 2022On the Paradox of Learning to Reason from Datacited by 93
  11. 2022HyperPrompt: Prompt-based Task-Conditioning of Transformerscited by 67
  12. 2022Exploring the Universal Vulnerability of Prompt-based Learning Paradigmcited by 56
3 more
  1. 2023Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraintscited by 31
  2. 2021Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companioncited by 4
  3. 2023Conversation Style Transfer using Few-Shot Learningcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.

6 papers.