What distance learning students want from an AI Digital Assistant

Bart Rienties, John Domingue, Subby Duttaroy, Christothea Herodotou, Felipe Maciel Tessarolo, Felipe Tessarolo and Denise Whitelock 2024. What distance learning students want from an AI Digital Assistant. Distance Education. Advanced online publication. https://doi.org/10.1080/01587919.2024.2338717

TitleWhat distance learning students want from an AI Digital Assistant
TypeJournal article
AuthorsBart Rienties, John Domingue, Subby Duttaroy, Christothea Herodotou, Felipe Maciel Tessarolo, Felipe Tessarolo and Denise Whitelock
Abstract

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students’ perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students’ perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalization and accessibility, together with emotional and social support. Students concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.

JournalDistance Education
ISSN0158-7919
1475-0198
Year2024
PublisherTaylor & Francis
Digital Object Identifier (DOI)https://doi.org/10.1080/01587919.2024.2338717
Web address (URL)https://doi.org/10.1080/01587919.2024.2338717
Publication dates
Published online28 May 2024

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