Track Program Chairs

Track description and topics of interest

Broadly defined, Artificial Intelligence and Smart Learning Environments represent a new wave of educational systems, involving an effective and efficient interplay of pedagogy, technology, and their fusion towards the betterment of learning processes. Artificial Intelligence has the potential to educate, train, and augment human productivity, making them better at their tasks and activities. Artificial Intelligence can also make a better quality of an individual’s work, resulting in better learning and teaching.

A learning environment can be considered smart when the learner is supported by adaptive and innovative technologies from childhood through formal education and continued during work and adult life where non-formal and informal learning approaches become primary means for learning. Smart learning environments are neither pure technology-based systems nor a particular pedagogical approach. They encompass various contexts in which students (and perhaps teachers) move from one context to another. So, they are perhaps an overarching concept for future academia. This perspective has the potential to overcome some of the traditions of institution-based instruction towards lifelong learning.

AISLE@ICALT2022 will explore various dimensions of applying artificial intelligence and the emerging smart learning environments, such as what makes a learning environment smart, challenges in the design and implementation of such environments in multiple and heterogeneous contexts, pedagogical and technological underpinnings, and the validation issues. Various components of this interplay include but are not limited to:

1. Pedagogy/didactics: instructional design, learning paradigms, teaching paradigms, environmental factors, assessment paradigms, social factors, policy
2. Emerging technology: innovative uses of mature technologies, interactions, adoption, usability, standards, and emerging/new technological paradigms (open educational resources, learning analytics, cloud computing, smart classrooms, etc.)
3. Fusion of pedagogy/didactics and technology: transformation of curriculum, transformation of teaching behaviour, transformation of learning, transformation of administration, transformation of schooling, best practices of infusion, piloting of new ideas.
4. AI governance and policy for smart learning: AI governance, AI risk management, AI accountability, AI self-surveillance, biases in AI Algorithms, use and misuse of AI, AI on societal impact.
5. AI technology & practice for smart learning: Explainable AI, interpretable ML, flexibility and contextual understanding by humans, explanation and comprehensible by humans, intelligent agent (assistants), automated conversational robot (Chabot), AI-enabled personalization.

Track Program Committee