We host a physical full-day workshop colocated with the LAK 2024 conference to discuss specific algorithmic and machine learning issues for optimizing human learning.


What should we learn next? In this current era where digital access to knowledge is cheap and user attention is expensive, a number of online applications have been developed for learning. These platforms collect a massive amount of data over various profiles, that can be used to improve learning experience: intelligent tutoring systems can infer what activities worked for different types of students in the past, and apply this knowledge to instruct new students. In order to learn effectively and efficiently, the experience should be adaptive: the sequence of activities should be tailored to the abilities and needs of each learner, in order to keep them stimulated and avoid boredom, confusion and dropout. In the context of reinforcement learning, we want to learn a policy to administer exercises or resources to individual students.

Educational research communities have proposed models that predict mistakes and dropout, in order to detect students that need further instruction. Such models are usually calibrated on data collected in an offline scenario, and may not generalize well to new students. There is now a need to design online systems that continuously learn as data flows, and self-assess their strategies when interacting with new learners. These models have been already deployed in online commercial applications (ex. streaming, advertising, social networks) for optimizing interaction, click-through-rate, or profit. Can we use similar methods to enhance the performance of teaching in order to promote lifetime success? When optimizing human learning, which metrics should be optimized? Learner progress? Learner retention? User addiction? The diversity or coverage of the proposed activities? What are the issues inherent to adapting the learning process in online settings, in terms of privacy, fairness (disparate impact, inadvertent discrimination), and robustness to adversaries trying to game the system?

Student modeling for optimizing human learning is a rich and complex task that gathers methods from machine learning, cognitive science, educational data mining and psychometrics. This workshop welcomes researchers and practitioners in the following topics (this list is not exhaustive):


The conference starts on Tuesday March 19 at 9 AM JST.

Towards building a simulated agent [slides]
Aritra Ghosh (Meta), Andrew Lan (University of Massachussetts Amherst)
Tutorial 1
Adaptive Learning Systems and their Overall Effectiveness in Learning Gains [slides]
Yizhu Gao (University of Georgia)
Tutorial 2
Challenges in Educational Recommender Systems [slides]
Yong Zheng (Illinois Institute of Technology)
Tutorial 3
Contextual Bandits and Reinforcement Learning with Human Feedback [slides]
Jill-Jênn Vie (Inria, France)


The Optimizing Human Learning workshop will be held in Kyoto.

Register now

Important Dates

Dec 16 (extended), 2023 – AoE

Deadline for paper submissions - CFP on EasyChair

January 13, 2024 or before

Notification of acceptance

Feb 5, 2024

Early bird registration

March 19, 2024, 9 AM JST

Optimizing Human Learning Workshop

Call for Papers

Short papers

Between 2 and 3 pages

Full papers

Between 4 and 6 pages

Submissions can be made through EasyChair and should follow the LNCS format.

Workshop Topics



To contact us, join our Google group: optimizing-human-learning

Workshop Chairs

Yizhu Gao, University of Georgia
Samuel Girard, Inria, France
Hisashi Kashima, Kyoto University, Japan
Fabrice Popineau, CentraleSupélec & LRI, France
Jill-Jênn Vie, Inria, France
Yong Zheng, Illinois Institute of Technology, USA

Program Committee

Hisashi Kashima, Kyoto University, Japan
Fabrice Popineau, CentraleSupélec & LRI, France
Jill-Jênn Vie, Inria, France
Jacob Whitehill, Worcester Polytechnic Institute, USA