| 2024 | EDM | More, May not the Better: Insights from Applying Deep Reinforcement Learning for Pedagogical Policy Induction. | Gyuhun Jung, Markel Sanz Ausin, Tiffany Barnes, Min Chi |
| 2023 | AIED | A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics. | Markel Sanz Ausin, Mark Abdelshiheed, Tiffany Barnes, Min Chi |
| 2021 | AIED | Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. | Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi |
| 2020 | AIED | Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies. | Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi |
| 2020 | IJCAI | Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract). | Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi |
| 2019 | AIED | Hierarchical Reinforcement Learning for Pedagogical Policy Induction. | Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi |
| 2019 | EDM | Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System. | Markel Sanz Ausin, Hamoon Azizsoltani, Tiffany Barnes, Min Chi |
| 2019 | IJCAI | Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts. | Hamoon Azizsoltani, Yeo-Jin Kim, Markel Sanz Ausin, Tiffany Barnes, Min Chi |