Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education

Autor/inn/en	Sajja, Ramteja; Sermet, Yusuf; Cwiertny, David; Demir, Ibrahim
Titel	Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education
Quelle	In: International Journal of Educational Technology in Higher Education, 20 (2023), Artikel 42 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sajja, Ramteja) ORCID (Sermet, Yusuf) ORCID (Cwiertny, David) ORCID (Demir, Ibrahim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41239-023-00412-7
Schlagwörter	College Students; Artificial Intelligence; Teaching Assistants; Intelligent Tutoring Systems; Information Seeking; College Faculty; Teaching Load + Suchen Sie Ihr Suchwort? Collegestudent; Künstliche Intelligenz; Intelligentes Tutorsystem; Informationserschließung; Fakultät; Lehrdeputat
Abstract	Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due to insecurities or scheduling conflicts, which can lead to missed opportunities for questions. To support self-paced learning and encourage creative thinking skills, academic institutions must redefine their approach to education by offering flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students. Its GPT-3-based knowledge discovery component and the generalized system architecture are presented accompanied by a methodical evaluation of the system's accuracy and performance. (As Provided).
Anmerkungen	BioMed Central, Ltd. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://www.springer.com/gp/biomedical-sciences
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01