# A Transparency Index Framework for AI in Education

arXiv: https://arxiv.org/abs/2206.03220 · HTML: https://mlsanity.com/paper/2206.03220/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 76 references that are in the map.
- First posted on arXiv 9 May 2022.

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

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Transparency: Motivations and Challenges](https://mlsanity.com/paper/1708.01870/) (2017)

## Built on by (most cited first, 13 of 13)

- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations](https://mlsanity.com/paper/2403.03008/) (2024)
- [Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring](https://mlsanity.com/paper/2401.08517/) (2024)
- [The Accountability Paradox: How Platform API Restrictions Undermine AI Transparency Mandates](https://mlsanity.com/paper/2505.11577/) (2025)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Ethical AI Governance: Methods for Evaluating Trustworthy AI](https://mlsanity.com/paper/2409.07473/) (2024)
- ["Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom](https://mlsanity.com/paper/2503.02885/) (2025)
- [A Meta-Analysis of LLM Effects on Students across Qualification, Socialisation, and Subjectification](https://mlsanity.com/paper/2509.22725/) (2025)
- [Multi-Stakeholder Alignment in LLM-Powered Collaborative AI Systems: A Multi-Agent Framework for Intelligent Tutoring](https://mlsanity.com/paper/2510.23245/) (2025)
- [Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment](https://mlsanity.com/paper/2506.02046/) (2025)
- [Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems](https://mlsanity.com/paper/2606.08030/) (2026)
- [AI Transparency: Governance Compliance or Stakeholder Requirements?](https://mlsanity.com/paper/2606.30652/) (2026)

## Abstract (arXiv)

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.