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@ICALT2021 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

Nian-Shing CHEN, National Yunlin University of Science and Technology, Taiwan
Patricia A. JAQUES, University of Vale do Rio dos Sinos, Brazil
Junfeng YANG, Hangzhou Normal University, China
Stephen J.H. YANG, National Central University, Taiwan
Jorge Luis BACCA ACOSTA, University of Girona, Spain
Farshad BADIE, Aalborg University, Denmark
Ryan BAKER, University of Pennsylvania, USA
Débora BARBOSA, Feevale University, Brazil
Jorge BARBOSA, University of Vale do Rio dos Sinos, Brazil
Philippe BLACHE, Centre National de la Recherche Scientifique, France
Irene Y.L. CHEN, National Changhua University of Education, Taiwan
Maria-Iuliana DASCALU, University Politehnica of Bucharest, Romania
Ruiqi DENG, Hangzhou Normal University, China
Diego DERMEVAL, Federal University of Alagoas, Brazil
Fabiano DORÇA, Federal University of Uberlandia, Brazil
Wenxiang FAN, Hangzhou Normal University, China
Brendan FLANAGAN, Kyoto University, Japan
Isabela GASPARINI, Santa Catarina State University, Brazil
Gheorghita GHINEA, Brunel University London, UK
Mohammad Nehal HASNINE, Hosei University, Japan
Yue HU, Hangzhou Normal University, China
Anna HUANG, National Central Univerity, Taiwan
Chester HUANG, National Kaohsiung University of Science and Technology, Taiwan
Hazra IMRAN, The University of British Columbia, Canada
Yen-Ting LIN, National Pingtung University, Taiwan
Tze-Chang LIU, National Chung Hsing University, Taiwan
Jia-Jiunn LO, Chung Hua University, Taiwan
Leonardo MARQUES, Federal University of Alagoas, Brazil
Eleandro MASCHIO, Federal University of Technology, Paraná, Brazil
Tatsunori MATSUI, Waseda University, Japan
Sean Wolfgand MATSUI SIQUEIRA, Federal University of the State of Rio de Janeiro, Brazil
Riichiro MIZOGUCHI, Japan Advanced Institute of Science and Technology, Japan
Roger NKAMBOU, University of Quebec, Canada
Bernardo Pereira NUNES, The Australian National University, Australia
Magalie OCHS, Aix-Marseille Université, France
Hiroaki OGATA, Kyoto University, Japan
Eliseo REATEGUI, Federal University of Rio Grande do Sul, Brazil
Helena REIS, Federal University of Parana, Brazil
Rachel REIS, Federal University of Viçosa, Brazil
Kaoru SUMI, Future University Hakodate, Japan
Shu-Ming WANG, Chinese Culture University, Taiwan
Leandro Krug WIVES, Federal University of Rio Grande do Sul, Brazil
Jiun-Yu WU, National Yang Ming Chiao Tung University, Taiwan
Guangtao XU, Hangzhou Normal University, China
Tosh YAMAMOTO, Kansai University, Japan
Albert YANG, Kyoto University, Japan
Christopher YANG, Kyoto University, Japan
Chengjiu YIN, Kobe University, Japan
Xiaokun ZHANG, Athabasca University, Canada