# A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT

arXiv: https://arxiv.org/abs/2208.14582 · HTML: https://mlsanity.com/paper/2208.14582/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [FACE: Feasible and Actionable Counterfactual Explanations](https://mlsanity.com/paper/1909.09369/) (2019)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)

## Built on by (most cited first, 11 of 11)

- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [Predicting Learning Performance with Large Language Models: A Study in Adult Literacy](https://mlsanity.com/paper/2403.14668/) (2024)
- [Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI](https://mlsanity.com/paper/2409.15631/) (2024)
- [Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study](https://mlsanity.com/paper/2411.08906/) (2024)
- [Education in the age of Generative AI: Context and Recent Developments](https://mlsanity.com/paper/2309.12332/) (2023)
- [Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations](https://mlsanity.com/paper/2409.15971/) (2024)
- [ProfileXAI: User-Adaptive Explainable AI](https://mlsanity.com/paper/2510.22998/) (2025)
- [LLEXICORP: End-user Explainability of Convolutional Neural Networks](https://mlsanity.com/paper/2511.02720/) (2025)
- [The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty](https://mlsanity.com/paper/2607.26067/) (2026)
- [Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs](https://mlsanity.com/paper/2608.16914/) (2026)

## Abstract (arXiv)

A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models. This work practically demonstrates the proposed framework using predictive models for identifying at-risk learners of programme non-completion. The study then further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies in order to generate human-readable prescriptive feedback for those who are at risk using ChatGPT.