Building Socially Responsible Conversational Agents Using Big Data to Support Online Learning: A Case with Algebra Nation

Autor/inn/en	Li, Chenglu; Xing, Wanli; Leite, Walter
Titel	Building Socially Responsible Conversational Agents Using Big Data to Support Online Learning: A Case with Algebra Nation
Quelle	In: British Journal of Educational Technology, 53 (2022) 4, S.776-803 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Li, Chenglu) ORCID (Xing, Wanli) ORCID (Leite, Walter) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13227
Schlagwörter	Computer Mediated Communication; Group Discussion; Artificial Intelligence; Safety; Online Systems; Algebra; Mathematics Instruction; Computer Software; Models; Cooperative Learning; Integrated Learning Systems; Discourse Analysis; Language Usage; Benchmarking; Social Responsibility; High School Students + Suchen Sie Ihr Suchwort? Computerkonferenz; Gruppendiskussion; Künstliche Intelligenz; Sicherheit; Online; Mathematics lessons; Mathematikunterricht; Analogiemodell; Kooperatives Lernen; Diskursanalyse; Sprachgebrauch; Soziale Verantwortung; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin
Abstract	A discussion forum is a valuable tool to support student learning in online contexts. However, interactions in online discussion forums are sparse, leading to other issues such as low engagement and dropping out. Recent educational studies have examined the affordances of conversational agents (CA) powered by artificial intelligence (AI) to automatically support student participation in discussion forums. However, few studies have paid attention to the safety of CAs. This study aimed to address the safety challenges of CAs constructed with educational big data to support learning. Specifically, we proposed a safety-aware CA model, benchmarked with two state-of-the-art (SOTA) models, to support high school student learning in an online algebra learning platform. We applied automatic text analysis to evaluate the safety and socio-emotional support levels of CA-generated and human-generated texts. A large dataset was used to train and evaluate the CA models, which consisted of all discussion post-reply pairs (n = 2,097,139) by 71,918 online math learners from 2015 to 2021. Results show that while SOTA models can generate supportive texts, their safety is compromised. Meanwhile, our proposed model can effectively enhance the safety of generated texts while providing comparable support. [For the corresponding grantee submission, see ED619491.] (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01