Enhancing Educational Outcomes: Innovative Approaches to Human–AI Collaboration in Learning Environments

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) to support teaching and learning practices. With the growing presence of AI and large language models (LLMs) in educational contexts, exploring approaches to support human-centered AI has become a highly relevant and timely topic. 

This Special Issue focuses on “human-centered AI in education”, which refers to the approaches that prioritize the needs and agency of students and educators when designing and implementing AI technologies. In educational settings, human-centered AI highlights the accuracy, safety, and fairness of AI systems while also considering their influences on human identity and privacy. Exploring such approaches is relevant and timely considering the increasing integration of AI and large language models (LLMs) into educational settings. AI and LLMs have the potential to enhance learning and teaching experiences by providing personalized learning paths, timely feedback, and adaptive learning evaluations. However, their utilization also raises concerns related to data privacy, algorithmic bias, and the potential for reducing human involvement in educational processes. Therefore, this Special Issue will explore innovative approaches for facilitating the  use of human-centered AI in education, ensuring that the technology enhances rather than degrades the educational experience. Topics of interest for this Special Issue include (but are not limited to) the following: 

• Human–AI Collaboration in Education. In what ways can AI systems be designed to maximize their beneficial potential to humans? 
• AI’s Impact on Human Autonomy and Agency. How do AI systems influence the autonomy and agency of students and educators? How can we strike the balance between them? 
• Personalized Learning and Adaptive Systems. How can AI-driven personalized learning systems adapt to individual students’ needs and preferences? What are the benefits and challenges of adaptive learning technologies?
• Trustworthiness and Transparency of AI in Education. How can AI systems be made more transparent and understandable for educators, students, and other stakeholders? 
• Ethical Implications of AI in Education. What are the ethical challenges posed by AI and LLMs in educational settings? How can issues related to data privacy, algorithmic bias, and fairness be addressed? 
• Social and Cultural Impacts of AI in Education. What are the broader social and cultural implications of integrating AI in educational settings? How does AI influence educational equity and access?

We look forward to receiving your contributions.

Dr. Bo Pei
Guest Editor

Manuscript Submission Information

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• human-centered AI in education
• human–AI collaboration in education
• personalized learning
• AI trustworthy
• AI fairness
• ethical AI