# Scaling Evidence-based Instructional Design Expertise through Large Language Models

arXiv: https://arxiv.org/abs/2306.01006 · HTML: https://mlsanity.com/paper/2306.01006/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 5 of 5)

- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Learning gain differences between ChatGPT and human tutor generated algebra hints](https://mlsanity.com/paper/2302.06871/) (2023)
- [How Useful are Educational Questions Generated by Large Language Models?](https://mlsanity.com/paper/2304.06638/) (2023)
- [AI Insights into Theoretical Physics and the Swampland Program: A Journey Through the Cosmos with ChatGPT](https://mlsanity.com/paper/2301.08155/) (2023)

## Built on by (most cited first, 3 of 3)

- [LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance](https://mlsanity.com/paper/2501.00106/) (2024)
- [Bridging LMS and generative AI: dynamic course content integration (DCCI) for enhancing student satisfaction and engagement via the ask ME assistant](https://mlsanity.com/paper/2504.03966/) (2025)
- [ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities](https://mlsanity.com/paper/2508.14377/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implementation. We discuss the benefits and limitations of AI-driven content generation, emphasizing the necessity of human oversight in ensuring the quality of educational materials. This work is elucidated through two detailed case studies where we applied GPT-4 in creating complex higher-order assessments and active learning components for different courses. From our experiences, we provide best practices for effectively using LLMs in instructional design tasks, such as utilizing templates, fine-tuning, handling unexpected output, implementing LLM chains, citing references, evaluating output, creating rubrics, grading, and generating distractors. We also share our vision of a future recommendation system, where a customized GPT-4 extracts instructional design principles from educational studies and creates personalized, evidence-supported strategies for users' unique educational contexts. Our research contributes to understanding and optimally harnessing the potential of AI-driven language models in enhancing educational outcomes.