Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support

Abstract

Generative Artificial Intelligence (GenAI) holds a potential to advance existing educational technologies with capabilities to automatically generate personalised scaffolds that support students' self-regulated learning (SRL). While advancements in large language models (LLMs) promise improvements in the adaptability and quality of educational technologies for SRL, there remain concerns about the hallucinations in content generated by LLMs, which can compromise both the learning experience and ethical standards. To address these challenges, we proposed GenAI-enabled approaches for evaluating personalised SRL scaffolds before they are presented to students, aiming for reducing hallucinations and improving the overall quality of LLM-generated personalised scaffolds. Specifically, two approaches are investigated. The first approach involved developing a multi-agent system approach for reliability evaluation to assess the extent to which LLM-generated scaffolds accurately target relevant SRL processes. The second approach utilised the "LLM-as-a-Judge" technique for quality evaluation that evaluates LLM-generated scaffolds for their helpfulness in supporting students. We constructed evaluation datasets, and compared our results with single-agent LLM systems and machine learning approach baselines. Our findings indicate that the reliability evaluation approach is highly effective and outperforms the baselines, showing almost perfect alignment with human experts' evaluations. Moreover, both proposed evaluation approaches can be harnessed to effectively reduce hallucinations. Additionally, we identified and discussed bias limitations of the "LLM-as-a-Judge" technique in evaluating LLM-generated scaffolds. We suggest incorporating these approaches into GenAI-powered personalised SRL scaffolding systems to mitigate hallucination issues and improve the overall scaffolding quality.

References in corpus (15)

• Sparks of Artificial General Intelligence: Early experiments with GPT-4
• A Survey of Large Language Models
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
• Human-Centred Learning Analytics and AI in Education: a Systematic Literature Review
• Hallucination is Inevitable: An Innate Limitation of Large Language Models
• A Survey of Hallucination in Large Foundation Models
• How Language Model Hallucinations Can Snowball
• Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
• AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
• LLM Multi-Agent Systems: Challenges and Open Problems
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark
• An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4
• Humans or LLMs as the Judge? A Study on Judgement Biases
• Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge