Journal of Knowledge-Research Studies

From Retrieval to Mediation: Generative AI and the Reconfiguration of Information-Seeking Behavior Across the Life Course

Editor-in-Chief Lecture

10.22034/jkrs.2026.75721.1284

Abstract

Purpose: This editorial examines how generative artificial intelligence is reconfiguring information-seeking behavior by shifting users from interaction with predominantly retrieval-oriented systems toward engagement with active and conversational intermediaries that participate in searching, synthesizing, evaluating, and shaping information use across the life course.
Methodology: The article draws on an evidence-informed integrative synthesis of selected and credible research on information behavior, AI literacy, trust and verification, cognitive offloading and cognitive delegation, anthropomorphism, the AI divide, and family and institutional mediation. Evidence is examined across five life stages—childhood, adolescence, emerging adulthood, midlife, and later life—and subsequently organized through an evidence-informed SWOT framework and TOWS analysis.
Findings: Life stage does not directly determine GenAI-mediated information behavior; rather, its effects emerge through interaction with information and AI literacy, domain knowledge, metacognition, self-efficacy, prior experience, socioeconomic resources, and context of use. In childhood, source recognition and parental mediation are particularly salient; in adolescence, credibility assessment and ethical use; in emerging adulthood, the management of cognitive offloading and delegation; in midlife, efficiency, domain knowledge, and risk-sensitive trust; and in later life, access, self-efficacy, verification, and mediated use.
Conclusion: Educational and policy goals should move beyond increasing AI use and instead foster AI literacy, calibrated trust, verification competence, and retained epistemic agency.
Value: The editorial proposes a multilevel, life-course framework for understanding GenAI-mediated information behavior while avoiding deterministic generational interpretations.

Keywords

Main Subjects

References
Bick, A., Blandin, A., & Deming, D. J. (2026). The rapid adoption of generative AI. Management Science, 72(10), 8440–8456. https://doi.org/10.1287/mnsc.2025.02523
Bråten, I., Latini, N., & Strømsø, H. I. (2026). Exploring students’ (mis)conceptions about ChatGPT-generated text: A qualitative study. Education and Information Technologies, 31, 4637–4658. https://doi.org/10.1007/s10639-026-13938-w
Cohn, M., Pushkarna, M., Díaz, M., Cooper, J., Olanubi, G. O., Moran, J. M., Mengesha, Z., & Heldreth, C. (2026). Age and generational differences in anthropomorphism and trust in large language models. Frontiers in Cognition, 5, Article 1824836. https://doi.org/10.3389/fcogn.2026.1824836
Costanza, D. P., Rudolph, C. W., & Zacher, H. (2023). Are generations a useful concept? Acta Psychologica, 241, Article 104059. https://doi.org/10.1016/j.actpsy.2023.104059
Cruz, A. D. S., Manrique, P. C. J., & Mendeja, J. M. (2026). Exploring AI literacy, reliance, and ethical practices of secondary level students in using generative AI. Discover Education, 5, Article 938. https://doi.org/10.1007/s44217-026-01890-3
Güngör, B. Ş., & Güngör, A. (2026). Why parents of children aged 4–6 use generative AI: A qualitative interview study. Children and Youth Services Review, 189, Article 109196. https://doi.org/10.1016/j.childyouth.2026.109196
Hill, T., & Westbrook, R. (1997). SWOT analysis: It’s time for a product recall. Long Range Planning, 30(1), 46–52. https://doi.org/10.1016/S0024-6301(96)00095-7
Jiang, Y., & Chen, H. (2026). AI-based cognitive offloading by students in higher education in China. Computers in Human Behavior: Artificial Humans, 8, Article 100327. https://doi.org/10.1016/j.chbah.2026.100327
Kaur, A., & Chen, W. (2023). Exploring AI literacy among older adults. Studies in Health Technology and Informatics, 306, 9–16. https://doi.org/10.3233/SHTI230589
Langer, A., Martinez, S., Marshall, P. J., & Chein, J. (2026). Children’s susceptibility to content generated by artificial intelligence. Technology in Society, 86, Article 103303. https://doi.org/10.1016/j.techsoc.2026.103303
Latini, N., Bråten, I., & Strømsø, H. I. (2026). Mind over model? Students’ evaluation and use of ChatGPT-generated versus human-generated texts. Reading Research Quarterly, 61(1), Article e70087. https://doi.org/10.1002/rrq.70087
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392
Lin, R., Chen, Y., & Guan, M. (2026). Who do children trust? AI vs. human recommendations and the role of anthropomorphic design. Technological Forecasting and Social Change, 229, Article 124707. https://doi.org/10.1016/j.techfore.2026.124707
Liu, D., & Li, J. (2026). How Teacher–Learner–AI collaboration empowers AI literacy of older adults: A case study of an older adult school in China. Humanities and Social Sciences Communications. Advance online publication. https://doi.org/10.1057/s41599-026-08471-7
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
Lund, B. D., Teel, Z. A., Mohammed, Y., Jagathpally, A., & Wang, T. (2026). Artificial intelligence (AI) and information seeking: A comparative exploration of AI chatbots, search engines, and library resources as information sources among university students. Journal of Librarianship and Information Science. Advance online publication. https://doi.org/10.1177/09610006261438484
Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
Misiejuk, K., López-Pernas, S., Kaliisa, R., & Saqr, M. (2026). Cognitive offloading in student–AI collaboration: A longitudinal analysis of prompting strategies. Computers in Human Behavior Reports, 22, Article 101130. https://doi.org/10.1016/j.chbr.2026.101130
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
Quan, S., Tu-Shea, X., Ding, Y., Du, Y., Zheng, Q., & Gerdich, L. E. (2026). Conversational AI in children’s home literacy learning: Effectiveness, advantages, challenges, and family perception. Computers and Education: Artificial Intelligence, 10, Article 100549. https://doi.org/10.1016/j.caeai.2026.100549
Rudolph, C. W., Rauvola, R. S., Costanza, D. P., & Zacher, H. (2021). Generations and generational differences: Debunking myths in organizational science and practice and paving new paths forward. Journal of Business and Psychology, 36(6), 945–967. https://doi.org/10.1007/s10869-020-09715-2
Si, S., Qi, Y., Xu, J., & Qi, X. (2026). When thinking is outsourced: Cognitive offloading and the heterogeneity of critical thinking among Chinese university students using generative artificial intelligence. Journal of Intelligence, 14(7), Article 116. https://doi.org/10.3390/jintelligence14070116
Suárez, D., & García-Mariñoso, B. (2025). On the verge of a digital divide in the use of generative AI? Telecommunications Policy, 49(7), Article 102997. https://doi.org/10.1016/j.telpol.2025.102997
Sun, Z. L., & Chan, Y. Y. (2025). AI literacy development in middle school: Profiles and demographic predictors from an ABCE perspective. Computers and Education: Artificial Intelligence, 9, Article 100480. https://doi.org/10.1016/j.caeai.2025.100480
Tang, Z. R., Kim, S., Wang, H., Xue, B., & Zhou, Y. (2026). Teaching with AI: A dual qualitative study of parents’ intention to use generative AI for tutoring in China and the West. Computers & Education, 248, Article 105592. https://doi.org/10.1016/j.compedu.2026.105592
UNICEF Innocenti – Global Office of Research and Foresight. (2025). Guidance on AI and children 3.0: Updated guidance for governments and businesses to create AI policies and systems that uphold children’s rights. UNICEF Innocenti.
Wang, Y., Hong, A., & Zhang, Y. (2026). Same trust, different paths: How older adults reach similar AI trust through use and non-use. Interacting with Computers. Advance online publication. https://doi.org/10.1093/iwc/iwag040
Weihrich, H. (1982). The TOWS matrix—A tool for situational analysis. Long Range Planning, 15(2), 54–66. https://doi.org/10.1016/0024-6301(82)90120-0
Wilson, T. D. (2026). Modelling the impact of generative artificial intelligence on information behaviour research. Journal of Documentation, 82(7), 376–395. https://doi.org/10.1108/JD-03-2026-0150
Journal of Knowledge-Research Studies
Volume 5, Issue 3 - Serial Number 17
November 2026
Files
Share
How to cite
Statistics