Abstract
Discussion summaries have been considered important for promoting peer interactions in online discussion forums. However, most previous research only focused on student-generated summaries and their impact on learners in the summarizing roles. Even though recent artificial intelligence (AI) progress has demonstrated the possibility of generating decent forum post summaries, little attention has been paid to the educational implications of such auto-generated forum post summaries. To further understand the perceptions and reactions of students to AI-generated forum summaries, this research used a Wizard of Oz approach to collect students’ online discussion forum log data with and without the “AI-generated” summaries. The results indicated that making an auto-generated summary available for students might not necessarily boost their interactions and engagements, especially for inactive students. However, the summary could serve as a reminder for students to participate in the discussion forum in a timely manner. Future research is needed to investigate whether the timing of providing an auto-generated summary might influence its impact.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, A.X.: Building systems to improve online discussion. In: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 65–68. Association for Computing Machinery, New York (2018)
King, A.: Comparison of self-questioning, summarizing, and notetaking-review as strategies for learning from lectures. Am. Educ. Res. J. 29(2), 303–323 (1992)
Wise, A., Chiu, M.M.: Analyzing temporal patterns of knowledge construction in a role-based online discussion. Int. J. Comput.-Support. Collab. Learn. 6(3), 445–470 (2011)
Schellens, T., Van Keer, H., De Wever, B., Valcke, M.: Scripting by assigning roles: does it improve knowledge construction in asynchronous discussion groups? Int. J. Comput.-Support. Collab. Learn. 2(2), 225–246 (2007)
Peterson, A.T., Roseth, C.J.: Effects of four CSCL strategies for enhancing online discussion forums: Social interdependence, summarizing, scripts, and synchronicity. Int. J. Educ. Res. 76, 147–161 (2016)
Wise, A.F., Chiu, M.M.: The impact of rotating summarizing roles in online discussions: effects on learners’ listening behaviors during and subsequent to role assignment. Comput. Hum. Behav. 38, 261–271 (2014)
Zhang, A., Verou, L., Karger, D.: Wikum: bridging discussion forums and wikis using recursive summarization. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW‘17), pp. 2082–2096. Association for Computing Machinery, New York (2017)
Tarnpradab, S., Liu, F., Hua, K.: Toward extractive summarization of online forum discussions via hierarchical attention networks. In: Rus, V., Markov, Z. (eds.) The Thirtieth International Florida Artificial Intelligence Research Society Conference, pp. 288–292. The AAAI Press, Florida (2018)
Gerhana, Y.A., et al.: Text summarization using Textrank for knowledge externalization from Indonesian online discussion forums. In: 2021 7th International Conference on Wireless and Telematics (ICWT), pp. 1–7. IEEE, Danbung (2021)
Ramina, M., Darnay, N., Ludbe, C., Dhruv, A.: Topic level summary generation using BERT induced abstractive summarization model. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 747–752. IEEE, Madurai (2020)
Uddin, G., Baysal, O., Guerrouj, L., Khomh, F.: Understanding how and why developers seek and analyze API-related opinions. IEEE Trans. Softw. Eng. 47(4), 694–735 (2021)
Riek, L.D.: Wizard of Oz studies in HRI: a systematic review and new reporting guidelines. J. Hum. Robot Interact. 1(1), 119–136 (2012)
Gottipati, S., Shankararaman, V., Ramesh, R.: TopicSummary: a tool for analyzing class discussion forums using topic based summarizations. In: 2019 IEEE Frontiers in Education Conference (FIE), pp. 1–9. IEEE, Uppsala (2019)
Botarleanu, R.M., Dascalu, M., Allen, L.K., Crossley, S.A., McNamara, D.S.: Multitask summary scoring with longformers. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 756–761. Springer, Durham (2022)
Xiao, C., Shi, L., Cristea, A., Li, Z., Pan, Z.: Fine-grained main ideas extraction and clustering of online course reviews. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 294–306. Springer, Durham (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hao, X., Cukurova, M. (2023). Exploring the Effects of “AI-Generated” Discussion Summaries on Learners’ Engagement in Online Discussions. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-36336-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36335-1
Online ISBN: 978-3-031-36336-8
eBook Packages: Computer ScienceComputer Science (R0)