| Title: |
Personalization Capabilities of Current Technology Chatbots in a Learning Environment: An Analysis of Student-Tutor Bot Interactions |
| Language: |
English |
| Authors: |
Chee-Kit Looi; Fenglin Jia |
| Source: |
Education and Information Technologies. 2025 30(10):14165-14195. |
| Availability: |
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: |
Y |
| Page Count: |
31 |
| Publication Date: |
2025 |
| Document Type: |
Journal Articles; Reports - Research |
| Education Level: |
Higher Education; Postsecondary Education |
| Descriptors: |
Individualized Instruction; Artificial Intelligence; Technology Uses in Education; Educational Technology; Computer Mediated Communication; Man Machine Systems; Intelligent Tutoring Systems; Tutors; Interaction; Graduate Students; Educational Change; Student Needs |
| DOI: |
10.1007/s10639-025-13369-z |
| ISSN: |
1360-2357; 1573-7608 |
| Abstract: |
Since the advent of chatbots enabled by Generative AI such as ChatGPT, their application in the domain of education has been linked to promises of personalizing learning (PL). Through a study of conversational interactions of graduate students with such chatbots, this paper provides an empirical study of how current ChatGPT technologies can enable PL. We introduce a framework of levels of PL affordances enabled by the current state of ChatGPT technology. The case study uses multi-methods to analyze graduate student interactions with a ChatGPT tutor bot in discussions about the topic of educational reforms. Tutoring conversations of 51 students with the tutor bot are collected, and then analyzed to probe whether and how ChatGPT creates an individualized growth area for students, tailoring interactions to individual needs and supporting students to have a deeper understanding of the topic. This study contributes to the limited body of research on the evidence-based application of Generative AI to PL. |
| Abstractor: |
As Provided |
| Entry Date: |
2025 |
| Accession Number: |
EJ1476450 |
| Database: |
ERIC |