Short-Term Gains, Long-Term Gaps: the Impact of Gen-AI and Search Technologies on Retention
| dc.contributor.author | Akgün, Mahir | |
| dc.contributor.author | Toker, Sacip | |
| dc.contributor.other | Information Systems Engineering | |
| dc.date.accessioned | 2025-09-05T15:34:24Z | |
| dc.date.available | 2025-09-05T15:34:24Z | |
| dc.date.issued | 2025 | |
| dc.description | Google; Gates Foundation; Hewlett Packard Enterprise; Eedi; VitalSource; Duolingo English Test; Springer | en_US |
| dc.description.abstract | The rise of Generative AI (GenAI) tools, such as ChatGPT, has transformed how students access and engage with information, raising questions about their impact on learning outcomes and retention. This study investigates how GenAI (ChatGPT), search engines (Google), and e-textbooks influence student performance across tasks of varying cognitive complexity, based on Bloom’s Taxonomy. Using a sample of 123 students, we examined performance in three tasks: [1] knowing and understanding, [2] applying, and [3] synthesizing, evaluating, and creating. Results indicate that ChatGPT and Google groups outperformed the control group in immediate assessments for lower-order cognitive tasks, benefiting from quick access to structured information. However, their advantage diminished over time, with retention test scores aligning with those of the e-textbook group. For higher-order cognitive tasks, no significant differences were observed among groups, with the control group demonstrating the highest retention. These findings suggest that while AI-driven tools facilitate immediate performance, they do not inherently reinforce long-term retention unless supported by structured learning strategies. The study highlights the need for balanced technology integration in education, ensuring that AI tools are paired with pedagogical approaches that promote deep cognitive engagement and knowledge retention. © 2025 Elsevier B.V., All rights reserved. | en_US |
| dc.identifier.doi | 10.1007/978-3-031-99264-3_6 | |
| dc.identifier.isbn | 9789819671748 | |
| dc.identifier.isbn | 9789819664610 | |
| dc.identifier.isbn | 9783032008831 | |
| dc.identifier.isbn | 9789819671779 | |
| dc.identifier.isbn | 9783031949425 | |
| dc.identifier.isbn | 9789819666874 | |
| dc.identifier.isbn | 9783031936968 | |
| dc.identifier.isbn | 9783031941207 | |
| dc.identifier.isbn | 9789819669653 | |
| dc.identifier.isbn | 9783031961953 | |
| dc.identifier.issn | 1865-0937 | |
| dc.identifier.issn | 1865-0929 | |
| dc.identifier.scopus | 2-s2.0-105013019597 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-031-99264-3_6 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14411/10798 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
| dc.relation.ispartof | Communications in Computer and Information Science – Poster papers and late breaking results, workshops and tutorials, practitioners, industry and policy track, doctoral consortium, blue sky and wide AIED papers presented at the 26th International Conference on Artificial Intelligence in Education, AIED 2025 – Palermo – 335989 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | AI in Education | en_US |
| dc.subject | Bloom’s Taxonomy | en_US |
| dc.subject | Generative AI | en_US |
| dc.subject | Retention | en_US |
| dc.subject | Search Tools | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Education Computing | en_US |
| dc.subject | Engineering Education | en_US |
| dc.subject | Learning Systems | en_US |
| dc.subject | Search Engines | en_US |
| dc.subject | Students | en_US |
| dc.subject | Teaching | en_US |
| dc.subject | AI in Education | en_US |
| dc.subject | Bloom’s Taxonomy | en_US |
| dc.subject | Cognitive Task | en_US |
| dc.subject | Control Groups | en_US |
| dc.subject | E-Textbooks | en_US |
| dc.subject | Performance | en_US |
| dc.subject | Retention | en_US |
| dc.subject | Search Tools | en_US |
| dc.subject | Short-Term Gains | en_US |
| dc.subject | Textbooks | en_US |
| dc.title | Short-Term Gains, Long-Term Gaps: the Impact of Gen-AI and Search Technologies on Retention | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.institutional | Toker, Sacip | |
| gdc.author.scopusid | 57528167000 | |
| gdc.author.scopusid | 56608927500 | |
| gdc.coar.access | metadata only access | |
| gdc.coar.type | text::conference output | |
| gdc.description.department | Atılım University | en_US |
| gdc.description.departmenttemp | [Akgün] Mahir, Pennsylvania State University, University Park, United States; [Toker] Sacip, Department of Information Systems Engineering, Atilim University, Ankara, Turkey | en_US |
| gdc.description.endpage | 52 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 44 | en_US |
| gdc.description.wosquality | N/A | |
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