Making FAQ Chatbots More Inclusive: An Examination of Non-Native English Users' Interactions with New Technology in Massive Open Online Courses

Autor/inn/en	Han, Songhee; Liu, Min; Pan, Zilong; Cai, Ying; Shao, Peixia
Titel	Making FAQ Chatbots More Inclusive: An Examination of Non-Native English Users' Interactions with New Technology in Massive Open Online Courses
Quelle	In: International Journal of Artificial Intelligence in Education, 33 (2023) 3, S.752-780 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Han, Songhee)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-022-00311-4
Schlagwörter	Artificial Intelligence; MOOCs; Native Language; Computer Mediated Communication; Interaction; Sociolinguistics; Responses; Second Language Learning; Learning Analytics + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Computerkonferenz; Interaktion; Soziolinguistik; Zweitsprachenerwerb
Abstract	In this study, we examined interaction behaviors between a small number of participants in two massive open online courses (MOOCs) and an FAQ chatbot, focusing on the participants' native language markers. We used a binary native language marker (non-native English user vs. native English user) to distinguish between two groups in this multiple case study, which is based on the knowledge gained from a previous study reporting students' significantly different experiences based on the two language markers (Han & Lee, "Computer & Education," 179, Article 104395, 2022). We utilized a multimodal computer-mediated communication approach as a research guiding tool for examining the data--chatbot logs and the students' self-reported open-ended responses. We analyzed 42 students' (non-native English users: n = 27 and native English users: n = 15) precise language uses and constructional practices during student--chatbot interactions to investigate the similarities and differences between the two cases. Most importantly, we focused on the experiences of non-native English users with newly adopted technology, such as chatbots, highlighting a lack of attention to the challenges this group faces despite comprising the majority of MOOC students. By comparing the interactions between the two groups, we identified various student attitudes towards the chatbot with accompanying descriptions, hypothesizing that non-native English users' positioning effects might have contributed to their negative experiences with the chatbot. We also found that misinterpreted contextualized cues could influence non-native English users more adversely than native English users from an interactional sociolinguistics perspective. Thus, we provide chatbot response design strategies that could support non-native English users better, such as informing users of the chatbot's capabilities and limitations as early as possible during interactions. We argue that concentrating on the experiences of non-native English users with chatbots promotes a more inclusive learning environment regarding new technologies employed in MOOCs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01