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Developing an Inclusive Q&A Chatbot in Massive Open Online Courses

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13356))

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Abstract

This study aims to examine massive open online course (MOOC) students’ experiences with a natural language processing-based Q&A chatbot. Following the definition of ‘inclusive learning’ in MOOCs from the Universal Design of Learning approach, this study firstly compares students’ behavioral intentions before and after using the chatbot. Next, this study investigates students’ levels of several other learning experience domains after using the chatbot—teaching presence, cognitive presence, social presence, enjoyment, perceived use of ease, etc. After examining students’ possible disparate learning experiences in these domains, this study investigates how age, gender, region, and native language factors influence students’ learning experiences with the chatbot. Lastly, but most importantly, this study explores how demographic factors influence students’ perception of chatbot interactions. If any are found, this study will focus on possible negative demographic factors that affect only certain groups of students to further examine how to improve a Q&A chatbot for inclusive learning in MOOCs.

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Author information

Authors and Affiliations

  1. The University of Texas at Austin, Austin, TX, 78712, USA

    Songhee Han & Min Liu

Authors
  1. Songhee Han
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  2. Min Liu
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Corresponding author

Correspondence to Songhee Han .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

Appendices

Appendix A

Tentative student’ learning experience domains with the chatbot.

figure a

Appendix B

All interviews will be structured as open-ended conversations about their lived learning experiences with the chatbot. I will let participant responses shape the following questions asked during the interviews. Some examples of questions and prompts that I will use are as follows:

  1. 1.

    What was your experience like to use the chatbot?

  2. 2.

    Can you walk me through some of your thoughts about the chatbot’s responses that you just received?

  3. 3.

    Can you tell me more about what you said when you described […]?

  4. 4.

    What thoughts are standing out to you as you see the [erroneous] response here?

  5. 5.

    Earlier you mentioned that you were experiencing […] Are you noticing the same thing here?

  6. 6.

    Some people describe […] as they see this kind of response. Are you noticing something similar? Different? In what way?

  7. 7.

    Can you tell me more about this response that you received here? Do you find that it helps you or interferes with what you’re seeking?

  8. 8.

    Can you describe any feelings that were generated as you used the chatbot?

  9. 9.

    Did you notice that the chatbot cannot respond to this kind of question? Do you think there should be a response to this kind of question?

  10. 10.

    I am going to observe you while you use the chatbot in this session. During the observation, I noticed […]; did you realize that you were doing […]? Can you help me to understand more about […]?

  11. 11.

    Is there anything that you’d like to share about your experiences with the chatbot?

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Han, S., Liu, M. (2022). Developing an Inclusive Q&A Chatbot in Massive Open Online Courses. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_2

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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