Enhancing Learning Dynamics: Integrating Interactive Learning Environments and ChatGPT for Computer Networking Lessons
Open accessAbstract
The COVID-19 pandemic has catalyzed a rapid transformation in higher education, prompting institutions to embrace online learning and e-learning as essential mechanisms for academic continuity. In this context, Interactive Learning Environments (ILEs), augmented with generative AI chatbots, represent a promising approach to enhancing the effectiveness of virtual education and interactive learning. This paper investigates the efficacy of an ILE integrated with ChatGPT in the context of computer networking education. A pilot experiment was conducted with three graduate students with basic IT networking background. The study examined the impact of integrating ChatGPT within the ILE on students’ engagement, comprehension, and overall learning outcomes. Methodological details, including learning program design, learning outcomes assessment, ILE settings, and ChatGPT integration are presented. Results from pre- and post-assessment tests, quizzes, and students’ feedback on ChatGPT’s utility as a learning aid are discussed. The findings suggest a significant improvement in students’ comprehension and performance following their engagement with the ILE while using ChatGPT as a support tool. Despite a few drawbacks reported by the students in terms of the interfaces’ ease of use, and the timely response and appropriate content delivered by ChatGPT, they were overall satisfied with the experience. The students would recommend ChatGPT for learning purposes under certain controlled enviro…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In academic libraries, LLMs can enhance virtual and digital reference services, automate content summarization, and support interactive learning.
“In academic libraries these models can enhance virtual and digital reference services, automate content summarization, and support interactive learning (Ikwuanusi et al., 2023;Saeidnia, 2023;Soto et al., 2024).”
From Using Agentic <scp>AI</scp> to Enhance the Quality of Academic Libraries’ Responses for Student Queries (2025) · cites this paper as Soto et al., 2024
Cites
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · see citing works
The Difference between Emergency Remote Teaching and Online Learning
Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2024) · Handbook of Research in Online Learning · doi:10.1163/9789004702813_021 · Cited by 6,253 · see citing works
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024 · see citing works
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 1,826 · see citing works
The role of ChatGPT in higher education: Benefits, challenges, and future research directions
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.29 · Cited by 561 · see citing works
The impact of ChatGPT on higher education
Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1206936 · Cited by 519 · see citing works
Artificial intelligence in teaching and learning: what questions should we ask of ChatGPT?
Rospigliosi, P. ‘. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2180191 · Cited by 335 · see citing works
A systematic review of <scp>ChatGPT</scp> use in K‐12 education
Zhang, P., & Tur, G. (2024) · European Journal of Education · doi:10.1111/ejed.12599 · Cited by 280 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education
Mak, J., Nakatumba-Nabende, J., Clear, T., Clear, A., Abluwi, I., Andrei, O., Angeli, L., MacNeil, S., Oyelere, S. S., Hale Rattigan, M., et al. (2025) · Proceedings of the 2025 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3760545.3783975 · Cited by 12 · see citing works
Wang, N., & Chou, Y. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1518 · Cited by 4 · see citing works
- Authors
- Soto, D., Higashida, M., Shirai, S., Ueda, M., & Uranishi, Y.
- Affiliations
- The University of Osaka (JP)
- Otemon Gakuin University (JP)
- Corresponding author
- Soto, David
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2024
- Volume
- 246
- Pages
- 3595-3604
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4404835226
- Cited by
- 10
- References
- 9
- Citations by year
- 2026: 6 · 2025: 4
- FWCI
- 3.81
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Funders
- Japan Society for the Promotion of Science (21K18505/23H01019)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
- Keywords
- ChatGPT, generative AI chatbots, interactive learning environment, computer networks education, online learning, student engagement, learning outcomes, student satisfaction, COVID-19 pandemic, graduate students
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
