ALL24
ALL24: 1st International Workshop on Adaptive Lifelong Learning
Co-located with AIED 2024
8-12 July
Recife, Brazil
This (half-day) workshop emphasizes the role of adaptive lifelong learning in the dynamic landscape of Artificial Intelligence in Education (AIED). The pervasive influence of digitization mandates continuous learning, responding to the challenges of automation and skill gaps. Learners and trainees play a pivotal role in the frontline of implementing AI in lifelong learning education. They grapple with significant demands, confronting information overload and the necessity to exhibit a high degree of flexibility in order to effectively adapt to the rapid changes and continuous evolution of new AI tools. By highlighting the virtues of adaptive lifelong learning, the workshop examines the impact of AI on a diverse spectrum of learners. Adaptive AI tools emerge as a source of support for bridging learning gaps, particularly in the post-pandemic landscape, offering essential support to learners through streamlined automation. Furthermore, the workshop emphasizes the critical necessity for adaptation in the evolving AIED domain, moving beyond its roots in computer science to encompass a more expansive educational perspective. With a specific focus on multi-criteria adaptive lifelong learning, the workshop advocates for collaboration among learners, educators, policymakers, researchers, and EdTech companies. The ultimate goal is to facilitate the development of relevant and evidence-based adaptive AI tools that significantly enhance lifelong learning.
The aim of this workshop is to unite a community of AIED researchers interested in various aspects of lifelong learning, particularly in adaptation and personalization in lifelong learning. We invite contributions covering all topics related to adaptive lifelong learning, with a specific focus on, but not limited to, the following list:
- Tailoring lifelong learning to various factors, including knowledge, skills, motivation, engagement, and learning objectives
- Extending the range of adaptation criteria beyond mere relevance, incorporating factors such as fairness, diversity, bias, and pedagogical aspects
- Group-aware and context-aware adaptations in lifelong learning
- Lifelong learning adaptation involving multiple stakeholders, including learners, trainers, and EdTech companies
- Adaptive learning in Massive Open Online Courses (MOOCs)
- Quantifying learner’s engagement and dropout risk
- Adaptive or personalized nudging strategies within lifelong learning
- Multi-modal adaptive lifelong learning
- Adaptive educational games for enhancing lifelong learning
- Adaptive computer-assisted language learning
- Adaptive communication with learners (e.g. feedback, dashboard, etc)
- Chatbots for lifelong learning
- Adaptive simulations in work stations
- Explainable adaptations in lifelong learning
- Generating/composing adaptive learning trajectories
Types of submissions
We consider two types of submissions:
- Regular paper: 10-15 pages (including references)
- Short paper: 5-9 pages (including references)
The submission should be positioned within the current state of the field and articulate the contribution of the proposal within its specific application domain, even if preliminary results are presented. Detailed explanation of the methodology is expected, ensuring the replicability of experiments, and whenever feasible, a comparison with existing approaches in the literature should be provided.
Important dates
Paper submission deadline:
May 15th, 2024May 20th, 2024Author notification:
June 2nd, 2024June 6th, 2024Camera-ready version deadline: June 15th, 2024
Deadlines refer to 23:59 in the AoE (Anywhere on Earth) time zone.
Paper format
All submissions should be in English and are required to adhere to the the 1-column CEUR-ART style: Word template or Overleaf (LaTeX). Failure to comply with the formatting guidelines may result in rejection prior to review.
Submissions must be anonymous, as we adhere to the double-masked review process. Depending on the number of accepted papers, we will determine whether short papers should be presented through oral presentations or at a poster session. Submissions will be reviewed by the organizing committee.
Submission will be accepted through Easychair.
Publications
All accepted papers will be included in the workshop proceedings published on the workshop website and shall be submitted to CEUR-WS.org for online publication. Authors of selected papers will be invited to extend their submissions by up to 30% for potential inclusion in a special issue of a journal (name of the journal and special issue will be announced soon).
Proceedings of the First Workshop on Adaptive Lifelong Learning (ALL 2024)
Session I (14:00 – 15:30)
- Introduction (10′)
- Fostering Human-centered approaches when designing LA and AI solutions (20’+10′)
Paraskevi Topali - Design and Development of a Co-instructional Designer Bot Using GPT-4 to Support Teachers in Designing Instruction (20’+5′)
Kristina Krushinskaia - Using machine learning to predict the number of latent skills in online learning environments (20’+5′)
Changsheng Chen
Break (15:30 – 16)
Session II (16-18)
- Explainability and Control for Adaptive E-Learning Systems (20′ + 10′)
Jeroen Ooge - Personalized Learning in K-12 Education: Exploring Weak-Labels for a Random Forest-based Collaborative Filtering Approach (20′ + 5′)
Pedro Ilídio - Drop-out aware MOOCs recommendation (20′ + 10′)
Alireza Gharahighehi - Interactive discussion (30′)
- Conclusion (5′)
Organizing Committee
Program Committee
- Bita Akram, Assistant Professor, Computer Science, North Carolina State University
- Judy Kay, Professor, Computer Science, University of Sydney
- Diego Zapata-Rivera, Distinguished Presidential Appointee, Educational Testing Service, Princeton NJ
- Anaïs Tack, Postdoctoral Researcher, Language Technology, KU Leuven and uclouvain
- Ifeoma Adaji, Assistant Professor, Computer Science, The University of British Columbia
- Chen Sun, Research Associate, School of Environment, Education and Development, The University of Manchester
- Yang Shi, Assistant Professor, Computer Science, Utah State University
- Mirko Marras, Assistant Professor, Artificial Intelligence, University of Cagliari
- Wim van den Noortgate, Full Professor, Psychology and Educational Sciences, KU Leuven
- Irene-Angelica Chounta, Professor, Computer Science and Applied Cognitive Science, University of Duisburg-Essen
- Sameh Metwaly, Postdoctoral Researcher, Psychology and Educational Sciences, Itec, imec research group
- Ishari Amarasinghe, Postdoctoral Researcher, Artificial Intelligence, NOLAI
- Celine Vens, Professor, Machine learning and AI, KU Leuven, campus Kulak
- Felipe Kenji Nakano, Postdoctoral Researcher, Machine learning and AI, KU Leuven
For any questions regarding the workshop, please feel free to contact: alireza[dot]gharahighehi[at]kuleuven[dot]be