@article{park_mott_lee_gupta_jantaraweragul_glazewski_scribner_ottenbreit-leftwich_hmelo-silver_lester_2022, title={Investigating a visual interface for elementary students to formulate AI planning tasks}, volume={73}, ISSN={["2665-9182"]}, DOI={10.1016/j.cola.2022.101157}, abstractNote={Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this article, we propose a visual interface to enable upper elementary students (grades 3-5, ages 8-11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks.}, journal={JOURNAL OF COMPUTER LANGUAGES}, author={Park, Kyungjin and Mott, Bradford and Lee, Seung and Gupta, Anisha and Jantaraweragul, Katie and Glazewski, Krista and Scribner, J. Adam and Ottenbreit-Leftwich, Anne and Hmelo-Silver, Cindy E. and Lester, James}, year={2022}, month={Dec} }
 @article{ottenbreit-leftwich_glazewski_jeon_jantaraweragul_hmelo-silver_scribner_lee_mott_lester_2022, title={Lessons Learned for AI Education with Elementary Students and Teachers}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-022-00304-3}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Ottenbreit-Leftwich, Anne and Glazewski, Krista and Jeon, Minji and Jantaraweragul, Katie and Hmelo-Silver, Cindy E. and Scribner, Adam and Lee, Seung and Mott, Bradford and Lester, James}, year={2022}, month={Sep} }
 @article{fahid_acosta_lee_carpenter_mott_bae_saleh_brush_glazewski_hmelo-silver_et al._2022, title={Multimodal Behavioral Disengagement Detection for Collaborative Game-Based Learning}, volume={13356}, ISBN={["978-3-031-11646-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11647-6_38}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II}, author={Fahid, Fahmid Morshed and Acosta, Halim and Lee, Seung and Carpenter, Dan and Mott, Bradford and Bae, Haesol and Saleh, Asmalina and Brush, Thomas and Glazewski, Krista and Hmelo-Silver, Cindy E. and et al.}, year={2022}, pages={218–221} }
 @article{park_mott_lee_glazewski_scribner_ottenbreit-leftwich_hmelo-silver_lester_2021, title={Designing a Visual Interface for Elementary Students to Formulate AI Planning Tasks}, ISSN={["1943-6092"]}, DOI={10.1109/VL/HCC51201.2021.9576163}, abstractNote={Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this paper, we propose a visual interface to enable upper elementary students (grades 3–5, ages 8–11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a qualitative study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks.}, journal={2021 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC 2021)}, author={Park, Kyungjin and Mott, Bradford and Lee, Seung and Glazewski, Krista and Scribner, J. Adam and Ottenbreit-Leftwich, Anne and Hmelo-Silver, Cindy E. and Lester, James}, year={2021} }
 @article{mott_taylor_lee_rowe_saleh_glazewski_hmelo-silver_lester_2019, title={Designing and Developing Interactive Narratives for Collaborative Problem-Based Learning}, volume={11869}, ISBN={["978-3-030-33893-0"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-33894-7_10}, abstractNote={Narrative and collaboration are two core features of rich interactive learning. Narrative-centered learning environments offer significant potential for supporting student learning. By contextualizing learning within interactive narratives, these environments leverage students’ innate facilities for developing understandings through stories. Computer-supported collaborative learning environments offer students rich, collaborative learning experiences in which small groups of students engage in constructing artifacts, addressing disciplinary challenges, and solving problems. Narrative and collaboration have distinct affordances for learning, but combining them poses significant challenges. In this paper, we present initial work on solving this problem by introducing collaborative narrative-centered learning environments. These environments will enable small groups of students to collaboratively solve problems in rich multi-participant storyworlds. We propose a novel framework for designing and developing these environments, which we are using to create a collaborative narrative-centered learning environment for middle school ecosystems education. In the learning environment, students work on problem-solving scenarios centered on how to support optimal fish health in aquatic environments. Results from pilot testing the learning environment with 45 students suggest it supports the creation of engaging and effective collaborative narrative-centered learning experiences.}, journal={INTERACTIVE STORYTELLING, ICIDS 2019}, author={Mott, Bradford W. and Taylor, Robert G. and Lee, Seung Y. and Rowe, Jonathan P. and Saleh, Asmalina and Glazewski, Krista D. and Hmelo-Silver, Cindy E. and Lester, James C.}, year={2019}, pages={86–100} }
 @article{lee_rowe_mott_lester_2014, title={A Supervised Learning Framework for Modeling Director Agent Strategies in Educational Interactive Narrative}, volume={6}, ISSN={["1943-0698"]}, DOI={10.1109/tciaig.2013.2292010}, abstractNote={Computational models of interactive narrative offer significant potential for creating educational game experiences that are procedurally tailored to individual players and support learning. A key challenge posed by interactive narrative is devising effective director agent models that dynamically sequence story events according to players' actions and needs. In this paper, we describe a supervised machine-learning framework to model director agent strategies in an educational interactive narrative Crystal Island. Findings from two studies with human participants are reported. The first study utilized a Wizard-of-Oz paradigm where human “wizards” directed participants through Crystal Island's mystery storyline by dynamically controlling narrative events in the game environment. Interaction logs yielded training data for machine learning the conditional probabilities of a dynamic Bayesian network (DBN) model of the human wizards' directorial actions. Results indicate that the DBN model achieved significantly higher precision and recall than naive Bayes and bigram model techniques. In the second study, the DBN director agent model was incorporated into the runtime version of Crystal Island, and its impact on students' narrative-centered learning experiences was investigated. Results indicate that machine-learning director agent strategies from human demonstrations yield models that positively shape players' narrative-centered learning and problem-solving experiences.}, number={2}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES}, author={Lee, Seung Y. and Rowe, Jonathan P. and Mott, Bradford W. and Lester, James C.}, year={2014}, month={Jun}, pages={203–215} }