# Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions

arXiv: https://arxiv.org/abs/2306.00551 · HTML: https://mlsanity.com/paper/2306.00551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Are Language Models Worse than Humans at Following Prompts? It's Complicated](https://mlsanity.com/paper/2301.07085/) (2023)
- [An Inquisitive Code Editor for Addressing Novice Programmers' Misconceptions of Program Behavior](https://mlsanity.com/paper/2102.06098/) (2021)

## Built on by (most cited first, 1 of 1)

- [Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses](https://mlsanity.com/paper/2310.15317/) (2023)

## Abstract (arXiv)

Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students struggle with utilizing textbooks on programming to the maximum, with a possible reason being that the example programs provided as illustration of concepts in these textbooks don't offer sufficient interactivity for students, and thereby not sufficiently motivating to explore or understand these programming examples better. In our work, we explore the idea of enhancing the navigability of intelligent textbooks with the use of ``counterfactual'' questions, to make students think critically about these programs and enhance possible program comprehension. Inspired from previous works on nudging students on counter factual thinking, we present the possibility to enhance digital textbooks with questions generated using GPT.