Generative AI and multimodal educational data are reshaping how learning is supported, evaluated, and understood. Current AI-enhanced learning systems increasingly provide dashboards, feedback, recommendations, and automatically generated learning materials — but many still respond only after learners show difficulties or explicitly request help. More fundamentally, when AI can assist learners in searching, generating, analyzing, reflecting, receiving feedback, and adjusting strategies, we need to reconsider what "learning success" means in intelligent learning environments.

This workshop introduces Proactive Learning Success as an emerging paradigm for designing intelligent learning environments that diagnose learners' current states, model possible future learning needs, and provide timely, personalized, ethical, and pedagogically meaningful support (Zhao, Hwang, & Yin, 2027). It extends learning analytics, adaptive learning, and self-regulated learning by emphasizing forward-looking modelling, proactive intervention, human–AI collaboration, and evidence-based support for learners' cognitive, affective, behavioral, motivational, and social development.

Rather than treating learning success only as achievement, knowledge mastery, or final outcomes, the workshop views it as a dynamic and adaptive process involving engagement, motivation, strategy use, critical thinking, learner agency, deep understanding, and sustainable learning capacity.

List of Topics

• Proactive Learning Support: Proactive recommendation, scaffolding, prompting, feedback, and intervention systems; learner-state diagnosis and future-state modelling.
• Multimodal Learning Evidence: Multimodal evidence from behavioral, sensor, text, discourse, assessment, and classroom data; interpretation, validity, and explainability.
• GenAI Tutors & Human-AI Collaboration: GenAI-based tutors, agents, copilots, and personalized material generation; human-AI collaboration in learning environments.
• Learner Agency & Self-Regulation: Learner agency, self-regulation, co-regulation, motivation, and engagement; critical thinking, creativity, and lifelong learning.
• Teacher Orchestration & Dashboards: Teacher dashboards, human-in-the-loop orchestration, classroom implementation, pedagogical models, and learning design.
• Responsible & Ethical AI: Privacy, fairness, accessibility, accountability, and responsible AI use; scaling up in authentic educational settings.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome.

• Full papers (8–10 pages)
• Short papers (5–6 pages)
• Extended Summary (3–4 pages)

Submit via EasyChair: https://easychair.org/conferences/?conf=learningsuccessicce2

Template: All submissions to the workshop must follow the ICCE 2026 Template with MS Word format available.

Review process: The workshop follows a single-blind review process as ICCE conference: reviewers will know the authors' identities, but authors will not know the reviewers'. Anonymization is therefore not required.

Important Dates (GMT)

• August 11, 2026: Paper Submission Deadline
• September 1, 2026: Acceptance Notification
• September 15, 2026: Camera-ready Paper Deadline
• Nov 30 or Dec 1, 2026: Workshop Day

Organizers

• Chengjiu Yin, Kyushu University, Japan
• Huiyong Li, Kyushu University, Japan
• Fuzheng Zhao, Jilin University, China
• Albert C.M. Yang, National Chung Hsing University, Taiwan
• Christopher C.Y. Yang, National Taipei University of Education, Taiwan
• Yun-Fang Tu, Soochow University, Taiwan
• Bo Jiang, East China Normal University, China

Publication

Accepted papers will be presented at the workshop and published in the ICCE 2026 workshop proceedings with ISBN and will be indexed by Elsevier Bibliographic Database.

Venue

The workshop will be co-located with the 34th International Conference on Computers in Education (ICCE 2026), Rydges Latimer Hotel, Christchurch, New Zealand, Nov 30 - Dec 4, 2026.

Contact

All questions about submissions should be emailed to yinlab2024 [at] gmail.com.