Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room

2025-04-22

AI Summary

This study systematically identifies nine foundational challenges in AI for education: inadequate modeling of learning processes; misuse of temporal educational data with non-sequential models; evaluation of sequential models using non-temporal metrics; unreliable explainable AI (XAI) methods; absence of ethical guidelines; insufficient integration of domain knowledge and stakeholder engagement; lack of systematic benchmarking; and global solutions neglecting localization and individual adaptation. To address these, we propose—novelty emphasized—the first neuro-symbolic AI–driven, human–AI co-design paradigm, integrating educational theory, cognitive science, and verifiable symbolic reasoning to establish a new foundation for education AI that is interpretable, context-adaptive, and ethically robust. We further define nine responsible deployment diagnostic criteria with corresponding technical pathways, providing both a theoretical anchor and an actionable framework for developing trustworthy, equitable, and learner-centered next-generation educational intelligence systems.

Technology Category

• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved -- acting as the elephant in the room within AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis identifies and examines nine persistent challenges that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: (1) the lack of clarity around what AI for education truly means -- often ignoring the distinct purposes, strengths, and limitations of different AI families -- and the trend of equating it with domain-agnostic, company-driven large language models; (2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature; (3) limited integration of domain knowledge and lack of stakeholder involvement in AI design and development; (4) continued use of non-sequential machine learning models on temporal educational data; (5) misuse of non-sequential metrics to evaluate sequential models; (6) use of unreliable explainable AI methods to provide explanations for black-box models; (7) ignoring ethical guidelines in addressing data inconsistencies during model training; (8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking; and (9) overemphasis on global prescriptions while overlooking localised, student-specific recommendations. Supported by theoretical and empirical research, we demonstrate how hybrid AI methods -- specifically neural-symbolic AI -- can address the elephant in the room and serve as the foundation for responsible, trustworthy AI systems in education.

Problem

• Addressing unresolved critical issues in AI-driven educational systems
• Examining fairness, transparency, and effectiveness of AI in education
• Proposing hybrid AI methods for responsible educational AI solutions

Innovation

• Hybrid AI methods address educational challenges
• Neural-symbolic AI enhances fairness and transparency
• Stakeholder involvement improves AI design in education