ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)
On the map: LLM reasoning → Prompting and chain-of-thought reasoning
Abstract (arXiv)
We introduce a novel writing method called Probing Chain-of-Thought (ProCoT), which potentially prevents students from cheating using a Large Language Model (LLM), such as ChatGPT, while enhancing their active learning. LLMs have disrupted education and many other fields. For fear of students cheating, many have resorted to banning their use. These LLMs are also known for hallucinations. We conduct studies with ProCoT in two different courses with 65 students. The students in each course were asked to prompt an LLM of their choice with one question from a set of four and required to affirm or refute statements in the LLM output by using peer-reviewed references. The results show two things: (1) ProCoT stimulates creative/critical thinking and writing of students through engagement with LLMs when we compare the LLM-only output to ProCoT output and (2) ProCoT can prevent cheating because of clear limitations in existing LLMs, particularly ChatGPT, when we compare students' ProCoT output to LLM ProCoT output. We also discover that most students prefer to give answers in fewer words than LLMs, which are typically verbose. The average word counts for students in the first course, ChatGPT (v3.5), and Phind (v8) are 208, 391 and 383, respectively.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 9 of its 30 references that are in the map. First posted on arXiv 15 Dec 2023.
Builds on
9 of 30 references.
Closest ancestors · 1
Same direction, newest first
Other references · 8
- 2017Attention Is All You Needcited by 60,361
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
- 2023A Survey of Hallucination in Large Foundation Modelscited by 368
- 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
- 2020A Benchmark Dataset of Check-worthy Factual Claimscited by 27
- 2022State-of-the-art in Open-domain Conversational AI: A Surveycited by 5
Built on by
Citing papers per year: 2024: 4.
4 papers.
- 2024Bringing Generative AI to Adaptive Learning in Educationcited by 24
- 2024On the Limitations of Large Language Models (LLMs): False Attributioncited by 13
- 2024Generative AI and Teachers -- For Us or Against Us? A Case Studycited by 6
- 2024Instruction Makes a Differencecited by 2
- 2024On the Limitations of Large Language Models (LLMs): False Attribution6 Apr 2024
- 2024Generative AI and Teachers -- For Us or Against Us? A Case Study4 Apr 2024
- 2024Bringing Generative AI to Adaptive Learning in Education2 Feb 2024
- 2024Instruction Makes a Difference1 Feb 2024
Similar, not cited
Close in wording, no citation either way
- 2023Investigating the Efficacy of Large Language Models in Reflective Assessment Methods through Chain of Thoughts Prompting
- 2026Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
- 2025HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
- 2025ThinkPatterns-21k: A Systematic Study on the Impact of Thinking Patterns in LLMs
- 2025Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance
- 2024Evaluating Students' Open-ended Written Responses with LLMs: Using the RAG Framework for GPT-3.5, GPT-4, Claude-3, and Mistral-Large
- 2023Why Can Large Language Models Generate Correct Chain-of-Thoughts?
- 2026A Benchmark for LLM's Understanding of Middle School and High School Science Topics