Journal of Interactive and Intelligent Learning

Bridging Interactive Learning and Intelligent Technologies

As generative artificial intelligence and adaptive intelligent agents become deeply integrated into educational contexts, we are confronted with an issue that transcends the instrumentalist discourse of technology-enhanced teaching—ushering in an ontological inquiry into human-machine coexistence and the emergence of wisdom. Breakthroughs in large language models and multimodal interaction are propelling intelligent learning from one-way content delivery toward bidirectional dynamic negotiation. In an era of rapidly advancing technological intelligence, how can human-machine-in-the-loop collaboration simultaneously preserve algorithmic adaptivity and precision while safeguarding learner agency and avoiding cognitive outsourcing? This has become a defining cross-era question spanning education, computer science, and cognitive science.

At this pivotal paradigm shift, the Journal of Interactive and Intelligent Learning (JIIL) has come into being. The journal is grounded in exploring the underlying mechanisms through which "interaction" and "intelligence" mutually stimulate one another. We contend that genuine interaction is not merely surface-level interface clicking, but rather deep cognitive dialogue, affective perceptual intervention, and embodied interaction enabled by intelligent media—all aimed at activating learners' metacognitive monitoring. True intelligence, likewise, is reflected not solely in model scale and predictive accuracy, but also in deep perception of complex learning contexts, explainability of cognitive evolution, and extension of human-machine collaborative paradigms.

As a highly interdisciplinary frontier, JIIL is committed to breaking down disciplinary silos and building a dialogue platform that bridges technological development and educational mechanisms. We are interested not only in computational breakthroughs in generative AI, intelligent tutoring systems, and data mining, but also encourage rigorous inquiry from the perspectives of learning psychology and educational ethics. Inspiring education through intelligence, reaching farther through interaction—JIIL aspires to join scholars worldwide in upholding rigorous academic ethics and peer review standards, jointly elucidating the scientific logic of human-machine collaborative learning, and collectively envisioning a new landscape of human-machine symbiotic education in the digital intelligence era.

Research Interests: Educational Data Mining, Learning Analytics, Text Mining. Professor & Doctoral Supervisor at Central China Normal University. Published extensively in top-tier journals with significant impact in the field of learning analytics and educational data mining.