Lab Projects
Lab Projects
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Enhancing Self-Regulation in STEM Learning—A Game-Based Multi-Agent Approach
This project focuses on developing a method for teaching STEM concepts, such as parallel programming, through a game-based approach that integrates a chat-based LLM agent and an open player model (developed through process mining of players data) to encourage players to reflect on and critically evaluate their strategies in relation to others' problem-solving methods.
Key Personnel: PI: Prof. Magy Seif El-Nasr, Co-PIs: Prof. Roger Azevedo (UCF), Prof. Tyler Sorensen (UCSC), Prof. Brian Smith (BC), Prof. Jichen Zhu (ITU). Students: Sai Siddartha Maram, Devon McKee, Cameron Marano, and Jiahong Li. Post Doc: Zhiyu Lin.
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Impact:
Impact: This research contributes to the fields of intelligent tutoring systems and game-based learning showing a new approach and system for open learner models that reveals the problem-solving process increasing opportunities for reflection and, thereby enhancing their learning experiences.
Funding:
This project is funded by NSF Award Numbers: 2302778 and 1917855.
Outcomes:
For videos and full list of publications, check out Project Website
McKee, D., Lin, Z., Fox, B., Li, J., Zhu, J., Seif El-Nasr, M., and Sorensen, T. (2026). Parallel X: Redesigning of a Parallel Programming Educational Game with Semantic Foundations and Transfer Learning. SIGCSE.
Maram, S. S., Kleinman, E., Villareale, J., Zhu, J., Seif El-Nasr, M. (2024). ”Ah! I ”see”—Facilitating Process Reflection in Gameplay through a Novel Spatio-Temporal Visualization System. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 24), May 16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA.
Kleinman, E., Shergadwala, M., Teng, Z., Villareale, J., Baryant, A., Zhu, J., Seif El-Nasr, M. (2022). Analyzing Students’ Problem-Solving Sequences: A Human-in-the-Loop Approach. Journal of Learning Analytics.
Kleinman, E., Villareale, J., Shergadwala, M., Teng, Z., Bryant, A., Zhu, J., Seif El-Nasr, M. (2023). ”What else can I do?” Examining the Impact of Community Data on Adaptation and Quality of Reflection in an Educational Game. CHI 2023.
StudyCrafter: AI-Powered Research Methods Education
StudyCrafter is an intelligent platform that empowers students to design, conduct, and analyze research studies with the aid of AI, transforming research methods education into an active, engaging experience.
Key Personnel: PI: Prof. Casper Harteveld (Northeastern University); Co-PIs: Prof. Magy Seif El-Nasr, Prof. Camillia Matuk (UCSC), Prof. Steven Sutherland (U. Houston, Clear Lake), Prof. Eddie Melcer (UCSC), and Elin Carstensdottir (UCSC). Students: Sai Siddartha Maram. Post Doc: Zhiyu Lin.
More about the Project
Impact:
Impact: This research contributes to the field of education, exploring the use of game-based constructionist methods and AI methods to help students understand and learn about research methods.
Funding:
This project is funded by NSF Award Numbers: 2142497
Resources:
Outcomes:
Shao A. A., Maram, S. S., Matuk, C., Shirshekar, S., Carstendottir, E., Melcer, E., Sutherland, S., Seif El-Nasr, M., and Harteveld, C. (2025). Strategies for Self-Regulated Learning with AI in an Undergraduate Research Methods Course. ICLS Conference.
Sai Siddartha Maram, Anna Amato, Giovanni Maria Troiano, Steven Sutherland, Camillia Matuk, Edward Melcer, Elin Carstensdottir, Casper Harteveld, and Magy Seif El-Nasr. (2024). An Instructor’s Lens into the Role of AI in Teaching Experimental Research via Gamification. SAC: 39th ACM SIGAPP Symposium on Applied Computing.
INSPECT
INSPECT is an interactive visualization tool for generating player journey maps, helping researchers understand player behavior and experience. It also applies to customer journey maps for websites and apps.
Key Personnel: PI: Prof. Magy Seif El-Nasr, Jimmy Teng.
More about the Project
Impact:
Impact: This research contributes a system called INSPECT, an interactive visualization system, that transforms messy gameplay logs into clear visual roadmaps that reveal not just what players do, but how they do it—helping developers spot where players get stuck and coaches identify winning strategies.
Resources:
Outcomes:
Teng, J. and Seif El-Nasr, M. (2026). Unlocking Player Strategy: A Visual Journey Into Players’ Problem-Solving Behaviors. IEEE Computer Column.
Teng, J., Pfau, J., Maram, S., Seif El-Nasr, M. (2024). Interactive Player Journeys: Co-designing a Process Visualization System to Video Game Analytics. FDG.
Z. Teng, J. Pfau and M. S. El-Nasr. (2023). Visualization-based Iterative Segmentation to Augment Video Game Analytics, 2023 IEEE Conference on Games (CoG), Boston, MA, USA, 2023, pp. 1-2, doi: 10.1109/CoG57401.2023.10333151
Teng, Z., Pfau, J., Maram, S. S., Seif El-Nasr, M. (2022). Player Segmentation with INSPECT: Revealing Systematic Behavior Differences within MMORPG and Educational Game Case Studies. CHI Play Workshop.
Activision
Strategic Partnership with Activision: Defining the Future of AI in Game User Research. This ongoing collaboration focuses on designing and implementing a next-generation GUR pipeline, leveraging AI to derive deeper insights into player behavior.
Key Personnel: PI: Prof. Magy Seif El-Nasr, Dr. Emily Chen, Jiahong Li
Discourse Analyis for Co-Learning in Games
This project pioneers a discourse-centered, triangulated methodology to understand how player-to-player communication in serious games fosters collaborative learning, shared understanding, and collective action for real-world challenges like wildfire preparedness.
Key Personnel: PI: Prof. Magy Seif El-Nasr, Dr. Mario Escarce, Dr. Emily Chen, Shivam Shulka, Darian Lee.
More about the Project
Impact:
Impact: This research contributes a methodological approach to track and uncover how players co-learn together to uncover how the game encourages learning.
Outcomes:
Methodological Innovation: Developed a robust, transferable model for evaluating serious games that moves beyond simple outcomes to reveal the process of learning as it happens through dialogue.
Escarce, M., Johns, MJ., Lu, Yiyang, Toledo, A., Lai, T., Ho, B., Dhandapani, K., Isbister, K., and Seif El-Nasr, M. (2025). Fostering Collaborative Knowledge-Building and Resilience Through Player Discourse in Serious Games for Wildfire Preparedness. Joint Conference on Serious Games.
Emotion Regulation and Resilience
This project explores the use of AI and games to increase resilience through emotion and interpersonal regulation and mindfulness practices.
Key Personnel: PI. Prof. Magy Seif El-Nasr, Dr. Mahnaz Roshanaei, Atieh Kashani, Ulia Zaman, Shivam Shulka, Zhiyu Lin.
More about the Project
Impact:
Impact: This research contributes an AI system to help with regulation and mindfulness.
Resources:
Project Resilience - explored the use of Alternate Reality Games to measure resilience and emotion regulation.
Outcomes:
Habibi, R., Pfau, J., Holmes, J., and Seif El-Nasr, M. (2022). EAI: Empathetic AI for Empowering Resilience in Games. AIIDE Workshop on Experimental AI in Games.
Habibi, R., Maram, S., Pfau, J., Wei, J., Sisodiya, S., Kashani, A., Carstensdottir, E., and Seif El-Nasr, M. (2022). A Data-Driven Design of AR Alternate Reality Games to Measure Resilience. HCII (Best Paper)
Habibi, R., Pfau, J., Maram, S. S., Li, J., Larsen, B., Xu, J., ... & El-Nasr, M. S. (2023, April). Under pressure: A multi-modal analysis of induced stressors in games for resilience. In Proceedings of the 18th International Conference on the Foundations of Digital Games (pp. 1-10).
Kashani, A., Pfau, J., & El-Nasr, M. S. (2023, September). Assessing the Impact of Personality on Affective States from Video Game Communication. In 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) (pp. 1-7). IEEE.
Listening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback
This project explores the use of LLMs to help students reflect on course content weekly and to provide instructors with feedback on content and pacing. We are also investigating its use to provide a method for tracking and assessing learning of soft skills in terms of group work, such as communication, collaboration, goal setting, conflict resolution, and interpersonal regulation.
Key Personnel: PI. Prof. Magy Seif El-Nasr, Sai Siddartha Maram, Ulia Zaman
Instructors: Noah Wardrip-fruin, Mahanz Roshanaei.
More about the Project
Impact:
Impact: This research contributes an LLM system that can help revolutionize teaching, providing instructors with just-in-time feedback to enhance and adapt their materials and teaching methods. At the same time, it gives students autonomy to express and reflect on course content.
Resources:
Github page [coming soon]
Outcomes:
Maram, S., Zaman, U., and Seif El-Nasr, M. (2025). Listening with Language Models: Using LLMs to Collect and Interpret
Classroom Feedback. MIT AI & Education Summit.
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