Dear Colleagues,

In recent years, a fundamental transformation has occurred in education due to the increased integration of novel technologies such as Educational Data Mining (EDM), Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) in supporting the teaching and learning practices in current educational settings. Increasing concerns have been raised regarding the reliability in the use of these technologies, from the conceptualization of problems to the collection of data to the construction of models and to the generation and interpretation of results. Immediately addressing these concerns is highly needed, especially in the time for realizing full potential of the artificial intelligence (AI)-related techniques in education.

This Special Issue focuses on “trustworthiness”, which refers to all stakeholders, including individual stakeholders (i.e., users, developers, and researchers), organizational stakeholders, and national/international stakeholders engaged in making laws, rules, and regulations. In educational settings, the trustworthiness of AI emphasizes the accuracy and safety of the AI system as well as the corresponding impacts on human autonomy and privacy and whether they are treated fairly regardless of demographic backgrounds. Topics of interest for this Special Issue include (but are not limited to):

• Human-centered values. How can AI systems empower educators and support them to make informed decisions about student learning?
• Transparency and Explainability. How can AI help educators understand how the system works? How does the system generate interpretable and actionable insights to support the instructional process?
• What metrics can be proposed to mitigate the potential negative impact of AI on teaching and learning? What mechanisms should be put in place to ensure the responsibility and accountability of AI systems’ behavior? What educational policies or theoretical frameworks should be proposed to promote the corresponding requirements in practice?
• Learning Analytical Dashboard. What kinds of visualization techniques can be employed for convey the analytical insights to educator? What kinds of learning information should be emphasized and visualized to support the better understanding of student learning?
• AI bias evaluation and mitigation. What kinds of metrics can be proposed to evaluate the fairness level of the proposed AI model in the target analytical context? What are potential sources causing the bias, and how to mitigate the bias while ensuring accuracy?

I look forward to receiving your contributions.

Dr. Bo Pei
Guest Editor

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• AI in education
• educational data mining
• learning analytics
• learning analytical dashboard
• AI trustworthy
• AI fairness
• ethical AI