Conference Theme
Under the theme “Pedagogical, Inclusive and Ethical Use of AI in Education”, AIFE 2026 foregrounds three guiding principles that frame the conference programme and submissions.
- Pedagogical: advancing human-centred and learning-driven uses of AI that support deep understanding, inquiry, creativity, collaboration, and meaningful assessment, rather than substituting or automating teachers.
- Inclusive: ensuring equitable access and meaningful participation for all learners, including those from underrepresented, marginalised, or resource-constrained contexts, and attending to issues of language, culture, and accessibility.
- Ethical: fostering responsible, reflective, and sustainable engagement with AI, including attention to data governance, algorithmic bias, transparency, accountability, and emerging discourse on post-humanism and algorithmic governance.
With this anchoring theme, AIFE 2026 invites conversations on pressing questions that educators, policy makers, researchers, and industry partners are grappling with: How can AI be pedagogically engaged to enhance teaching, learning, and assessment? For whom does AI serve—or exclude—in education? How can AI be ethically and inclusively deployed to serve the enduring purposes of education?
Conference Strands at a Glance
Strands and Topics of Interest
Submissions should align with at least one of the following strands; cross-cutting or multi-strand submissions are welcome. The topics listed are indicative rather than exhaustive.
Strand 1: Foundations and Architectures of AI in Education
This strand explores conceptual, computational, and algorithmic foundations of AI that are relevant to educational applications.
Topics include:
- Learner modelling and user profiling for learning analytics and personalisation.
- Adaptive systems and intelligent tutoring architectures.
- Multimodal learning analytics (e.g., log data, video, speech, physiological data).
- Explainable and interpretable AI for educational stakeholders.
- Generative models and foundation models for learning, teaching, and assessment.
- Architectures that connect classroom, platform, and institutional data in ethical and secure ways.
Submissions that explicitly connect technical architectures with pedagogical meaning-making and classroom practice are particularly encouraged.
Strand 2: Human–AI Interaction and Learning Design
This strand focuses on the design, orchestration, and evaluation of AI systems that support effective and trustworthy human–AI collaboration in educational settings.
Topics include:
- Design of learner–AI dialogue and conversational agents for learning.
- Scaffolding and feedback design in AI-enhanced learning environments.
- User experience (UX) and interaction design for teachers and students.
- Classroom orchestration tools and dashboards that integrate AI recommendations.
- Design-based research and co-design with educators and learners.
We welcome studies that illuminate the pedagogical affordances and limitations of AI tools and offer design principles or actionable insights for practitioners.
Strand 3: Cognition, Emotion, and Self-Regulation
This strand invites research on how AI can support cognitive, motivational, and emotional aspects of learning in authentic contexts.
Topics include:
- AI-supported self-regulated learning and metacognitive prompting.
- Affective computing and emotion-aware learning environments.
- Motivation, engagement, and persistence in AI-mediated learning experiences.
- Adaptive feedback, hinting, and progression pathways.
- Well-being and mental health considerations in AI-supported learning.
Submissions should show how AI contributes to learners’ engagement, reflection, and emotional well-being, and discuss implications for design and practice.
Strand 4: Teaching, Professional Learning, and Assessment
This strand examines how AI is transforming teachers’ roles, pedagogical practices, professional learning, and assessment approaches.
Topics include:
- AI-supported lesson planning, curriculum design, and resource generation.
- Teacher professional development and professional learning communities around AI.
- Classroom orchestration, monitoring, and responsive teaching with AI tools.
- Assessment innovation: formative and summative assessment, feedback generation, and academic integrity in an AI-rich environment.
- Models of teacher–AI partnership that foster teacher agency, judgment, and professional growth.
Strand 5: Ethics, Society, and Post-Humanism
This strand foregrounds ethical and societal questions surrounding AI in education, across local, regional, and global contexts.
Topics include:
- Fairness, accountability, transparency, and sustainability in AI systems used in education.
- Data ethics, privacy, surveillance, and governance in educational settings.
- Algorithmic bias and its impact on access, opportunity, and outcomes.
- Critical perspectives on post-humanism, human–machine hybridity, and educational futures.
- Normative, philosophical, and policy perspectives on what should count as “good” AI for education.
Strand 6: Cross-Domain, Cultural, and Policy Innovations
This strand addresses AI’s impact across disciplines, sectors, cultures, and policy systems.
Topics include:
- Cross-domain innovation (e.g., STEM, arts, humanities, vocational education) with AI.
- Cultural adaptation and localisation of AI tools and content.
- AI for inclusion, accessibility, and equity in diverse educational systems.
- Policy frameworks, standards, and governance models for sustainable AI integration.
- Ecosystemic perspectives that link schools, higher education, industry, and communities.
Comparative, cross-country, and ecosystem-level studies are particularly encouraged.