Evaluating the Impact of Emotion-Aware AI Chatbots on Student Engagement and Programming Skills
Supervisor Name
Mamoun Nawahdah
Supervisor Email
mnawahdah@birzeit.edu
University
Birzeit University
Research field
Computer Science
Bio
Dr. Mamoun Nawahdah is an Assistant Professor of Computer Science at Birzeit University, Palestine. He earned his Ph.D. in Computer Science from the University of Tsukuba, Japan, where he was awarded the prestigious Japanese Government Scholarship (MEXT). His research focuses on human-computer interaction, educational technologies, and AI-driven learning systems. Dr. Nawahdah has published widely in international journals and conferences and actively contributes as a reviewer for leading venues in the field.
AI-driven educational chatbots have shown great promise in enhancing learning, particularly in programming education. However, their ability to recognize and respond to students' emotions remains underexplored. Emotional intelligence in chatbots is crucial to addressing challenges like cognitive overload, frustration, and lack of motivation, which are common in programming education. By adapting interactions to emotional cues, these chatbots can improve student engagement, motivation, and overall learning outcomes, creating a more supportive and effective learning environment. In our previous research [1][2], we identified key challenges in emotion recognition within chatbots, including managing complex human emotions, respecting privacy, and ensuring cultural sensitivity. Addressing these issues is vital for improving the effectiveness of AI chatbots in educational contexts. The goal of this study is to evaluate the impact of emotion-aware AI chatbots on student engagement, satisfaction, programming skills, and overall performance. We aim to explore how integrating emotional intelligence into chatbot interactions can enhance the learning experience, particularly in programming tasks, by providing personalized, emotion-sensitive feedback. We have designed an experimental study involving second-year Computer Science students who are developing their Object-Oriented Programming (OOP) skills. The participants will be assigned to one of two distinct learning setups: Real Teacher with a Chatbot Setup: In this setup, the teacher observes and assesses students' emotional states and adjusts their teaching approach accordingly. The chatbot provides additional support by offering personalized hints and encouragement based on the teacher's emotional insights, helping students overcome emotional barriers and enhance learning experiences. Avatar with a Chatbot Setup: In this setup, students will exclusively interact with an avatar-based AI chatbot that is equipped with advanced emotional intelligence capabilities. The chatbot will analyze students' emotional cues, such as frustration or confidence, through natural language processing and other inputs. It will then adapt its responses accordingly, offering tailored encouragement, hints, or detailed explanations to provide emotional support, enhance student engagement, and foster motivation throughout the learning process. Data will be collected through pre-surveys and post-surveys to assess changes in student engagement, satisfaction, and programming skills. Direct observations will be made during the experiment to evaluate chatbot usability, and students' performance will be measured by their problem-solving ability and task completion in programming activities. These data points will be analyzed using general statistical methods to identify patterns and relationships, providing insights into how the two learning setups influence student outcomes. By comparing changes across groups, the study aims to offer empirical evidence on the impact of emotion-aware AI chatbots in enhancing educational experiences and improving learning outcomes. This research aims to improve chatbot interactions through emotional adaptation, contributing to the development of more personalized and effective AI-driven educational tools. The findings could reshape programming education by showing how emotionally intelligent chatbots can help students overcome emotional barriers, reduce frustration, and sustain motivation, ultimately fostering more engaging and supportive learning environments. [1] H. Sawalha, M. Nawahdah, H. Jebara (2025) GUIDELINES FOR DEVELOPING PERSONALIZED AND EFFECTIVE VOICE CHATBOTS IN EDUCATION, INTED2025 Proceedings, pp. 5921-5929. [2] H. Jebara, M. Nawahdah, H. Sawalha (2025) EMOTION RECOGNITION IN EDUCATIONAL CHATBOTS: GUIDELINES FOR ENHANCING ENGAGEMENT AND PERSONALIZATION, INTED2025 Proceedings, pp. 5930-5937.
