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1. The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way

   Sanz_Ausin, Markel; Maniktala, Mehak; Barnes, Tiffany; Chi, Min (April 2023, International journal of artificial intelligence in education)

   While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the \textit{human-agent interactions} productive and fruitful. In real-life, complex, human-centric tasks, such as education and healthcare, data can be noisy and limited. Batch RL is designed for handling such situations where data is \textit{limited yet noisy}, and where \textit{building simulations is challenging}. In two consecutive empirical studies, we investigated Batch DRL for pedagogical policy induction, to choose student learning activities in an Intelligent Tutoring System. In Fall 2018 (F18), we compared the Batch DRL policy to an Expert policy, but found no significant difference between the DRL and Expert policies. In Spring 2019 (S19), we augmented the Batch DRL-induced policy with \textit{a simple act of explanation} by showing a message such as \textit{"The AI agent thinks you should view this problem as a Worked Example to learn how some new rules work."}. We compared this policy against two conditions, the Expert policy, and a student decision making policy. Our results show that 1) the Batch DRL policy with explanations significantly improved student learning performance more than the Expert policy; and 2) no significant differences were found between the Expert policy and student decision making. Overall, our results suggest that \textit{pairing simple explanations with the Batch DRL policy} can be an important and effective technique for applying RL to real-life, human-centric tasks. 

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2. Leveraging Fuzzy Logic Towards More Explainable Reinforcement Learning-Induced Pedagogical Policies on Intelligent Tutoring Systems

   Hostetter, J W; Abdelshiheed, M; Barnes, T; Chi, M (August 2023, IEEE)

   Deep Reinforcement Learning (Deep RL) has revolutionized the field of Intelligent Tutoring Systems by providing effective pedagogical policies. However, the ``black box'' nature of Deep RL models makes it challenging to understand these policies. This study tackles this challenge by applying fuzzy logic to distill knowledge from Deep RL-induced policies into interpretable IF-THEN Fuzzy Logic Controller (FLC) rules. Our experiments show that these FLC policies significantly outperform expert policy and student decisions, demonstrating the effectiveness of our approach. We propose a Temporal Granule Pattern (TGP) mining algorithm to increase the FLC rules' interpretability further. This work highlights the potential of fuzzy logic and TGP analysis to enhance understanding of Deep RL-induced pedagogical policies. 

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3. Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems.

   Abdelshiheed, M; Hostetter, J W; Barnes, T; Chi, M (July 2023, Springer)

   Intelligent Tutoring Systems (ITSs) leverage AI to adapt to individual students, and many ITSs employ \emph{pedagogical policies} to decide what instructional action to take next in the face of alternatives. A number of researchers applied Reinforcement Learning (RL) and Deep RL (DRL) to induce effective pedagogical policies. Much of prior work, however, has been developed \emph{independently} for a specific ITS and \emph{cannot directly be applied to another}. In this work, we propose a \textbf{M}ulti-\textbf{T}ask \textbf{L}earning framework that combines Deep \textbf{BI}simulation \textbf{M}etrics and DRL, named \textbf{MTL-BIM}, to induce a unified pedagogical policies for two different ITSs across different domains: logic and probability. Based on empirical classroom results, our unified RL policy performed significantly better than the expert-crafted policies and independently induced DQN policies on both ITSs. 

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4. Online Reinforcement Learning-Based Pedagogical Planning for Narrative-Centered Learning Environments

   https://doi.org/10.1609/aaai.v38i21.30365  

   Fahid, Fahmid Morshed; Rowe, Jonathan; Kim, Yeojin; Srivastava, Shashank; Lester, James (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data. 

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5. Get a Head Start: On-Demand Pedagogical Policy Selection in Intelligent Tutoring

   Gao, G; Yang, X; Chi, M (February 2024, AAAI)

   Reinforcement learning (RL) is broadly employed in humaninvolved systems to enhance human outcomes. Off-policy evaluation (OPE) has been pivotal for RL in those realms since online policy learning and evaluation can be high-stake. Intelligent tutoring has raised tremendous attentions as highly challenging when applying OPE to human-involved systems, due to that students’ subgroups can favor different pedagogical policies and the costly procedure that policies have to be induced fully offline and then directly deployed to the upcoming semester. In this work, we formulate on-demand pedagogical policy selection (ODPS) to tackle the challenges for OPE in intelligent tutoring. We propose a pipeline, EDUPLANNER, as a concrete solution for ODPS. Our pipeline results in an theoretically unbiased estimator, and enables efficient and customized policy selection by identifying subgroups over both historical data and on-arrival initial logs. We evaluate our approach on the Probability ITS that has been used in real classrooms for over eight years. Our study shows significant improvement on learning outcomes of students with EDUPLANNER, especially for the ones associated with low-performing subgroups. 

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