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Purpose <p>This study examines how different emotion-aware chatbot interaction modalities, particularly voice-based support augmented with an animated avatar, affect student engagement and code outcomes in undergraduate programming courses, compared with text-only and real-teacher support.</p> Methods <p>We conducted a controlled classroom experiment with Arabic-speaking computer science students at Birzeit University during a 60-minute Java programming task. Participants were randomly assigned to one of four instructional support conditions: text-based chatbot interaction, voice-based chatbot interaction, voice-based interaction with an animated avatar, or voice-based interaction with a real teacher. For all chatbot-based conditions, a Wizard-of-Oz methodology was employed in which a trained human operator simulated an emotion-aware chatbot to ensure consistent and realistic responses across modalities. Data collection included subjective measures (satisfaction, usability, and perceived engagement) and objective indicators (task completion, code readability, maintainability, and accuracy), along with affective signals captured throughout the session.</p> Results <p>The avatar-supported voice condition was associated with more stable positive affect, higher sustained engagement, and better-structured, more readable, and more maintainable code compared with text-based interaction. Across multiple objective outcomes, voice-based support showed advantages over text-only interaction, while the real-teacher voice condition demonstrated strong task completion performance. Differences in subjective usability and satisfaction followed similar trends but did not reach statistical significance.</p> Conclusion <p>The findings suggest that voice-based, emotionally expressive chatbot interfaces, particularly when combined with an animated avatar, can approximate aspects of supportive teacher presence while remaining scalable for large programming classes. Even when implemented using a Wizard-of-Oz approach, emotion-aware voice interactions show promise for improving engagement and selected code quality dimensions. These results offer design guidance for developing classroom-ready AI teaching assistants for programming education, with particular relevance for Arabic-speaking learners.</p>

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Enhancing programming education with emotion-aware chatbot interfaces: a Wizard-of-Oz study among Arabic-speaking university students

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

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  • Mamoun Nawahdah,
  • Hadeel Sawalha,
  • Hadeel Jabara

摘要

Purpose

This study examines how different emotion-aware chatbot interaction modalities, particularly voice-based support augmented with an animated avatar, affect student engagement and code outcomes in undergraduate programming courses, compared with text-only and real-teacher support.

Methods

We conducted a controlled classroom experiment with Arabic-speaking computer science students at Birzeit University during a 60-minute Java programming task. Participants were randomly assigned to one of four instructional support conditions: text-based chatbot interaction, voice-based chatbot interaction, voice-based interaction with an animated avatar, or voice-based interaction with a real teacher. For all chatbot-based conditions, a Wizard-of-Oz methodology was employed in which a trained human operator simulated an emotion-aware chatbot to ensure consistent and realistic responses across modalities. Data collection included subjective measures (satisfaction, usability, and perceived engagement) and objective indicators (task completion, code readability, maintainability, and accuracy), along with affective signals captured throughout the session.

Results

The avatar-supported voice condition was associated with more stable positive affect, higher sustained engagement, and better-structured, more readable, and more maintainable code compared with text-based interaction. Across multiple objective outcomes, voice-based support showed advantages over text-only interaction, while the real-teacher voice condition demonstrated strong task completion performance. Differences in subjective usability and satisfaction followed similar trends but did not reach statistical significance.

Conclusion

The findings suggest that voice-based, emotionally expressive chatbot interfaces, particularly when combined with an animated avatar, can approximate aspects of supportive teacher presence while remaining scalable for large programming classes. Even when implemented using a Wizard-of-Oz approach, emotion-aware voice interactions show promise for improving engagement and selected code quality dimensions. These results offer design guidance for developing classroom-ready AI teaching assistants for programming education, with particular relevance for Arabic-speaking learners.