Journal Screenshot

International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

High School Students’ Evaluation of AI-Generated Energy Recommendations: A Qualitative Study of Trust and Decision-Making

Guanqi Ma

http://dx.doi.org/10.6007/IJARBSS/v16-i10/29204

Open access

This paper examines the way high school students perceive the credibility of AI-generated energy recommendations and also justify how they choose to accept, modify or reject them. A qualitative design was taken and the semi-structured interviews with eight high school students were used. The respondents were asked three cases that concerned the use of washing machines, air conditioners, and energy-saving tips with explanatory details. The data were thematically analyzed. These results imply that the students have taken into account the applicability of recommendations to their domestic situation, the strength of evidence and coherence of explanations. Having knowledge about AI did not mean that participants trusted the specific recommendations. Household routines, comfort, and practical needs were also the factors that students took into account when determining whether to take the advice. Others wanted to get more information or confirmation prior to decision making. These results suggest the relevance of situational context, explanations that can be understood, and evidence in helping the students to evaluate AI generated energy recommendations. The research provides an understanding of the rationale of young users concerning the use of AI in making energy choices.

Grawitch, M. J., Winton, S. L., & Mudigonda, S. P. (2026). Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making. Computers in Human Behavior: Artificial Humans, 7, 100283. https://doi.org/10.1016/j.chbah.2026.100283
Henrique, B. M., & Santos, E., Jr. (2024). Trust in artificial intelligence: Literature review and main path analysis. Computers in Human Behavior: Artificial Humans, 2(1), 100043. https://doi.org/10.1016/j.chbah.2024.100043
Massagony, A., Pandit, R., & White, B. (2026). Effects of informational nudges in promoting household electricity conservation in Indonesia. Energy Policy, 208, 114932. https://doi.org/10.1016/j.enpol.2025.114932
McIlvennie, C., Sanguinetti, A., & Pritoni, M. (2020). Of impacts, agents, and functions: An interdisciplinary meta-review of smart home energy management systems research. Energy Research & Social Science, 68, 101555. https://doi.org/10.1016/j.erss.2020.101555
Oonk, A. S., Starke, A. D., & Willemsen, M. C. (2025). Goal setting and signposting in a Rasch-based recommender system to promote household energy conservation. Energy Efficiency, 18, 48. https://doi.org/10.1007/s12053-025-10332-2
Papenkordt, J., Ngonga Ngomo, A.-C., & Thommes, K. (2026). Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection? Behaviour & Information Technology, 45(10), 2094–2115. https://doi.org/10.1080/0144929X.2025.2568928
Rahman, S. M. T., Siemon, D., & Ruotsalo, T. (2026). Persuasive explanations for recommender systems: How explanations can influence users’ choices? International Journal of Human-Computer Studies, 208, 103720. https://doi.org/10.1016/j.ijhcs.2025.103720
Samancioglu, N., Castaño-Rosa, R., Väänänen, K., & Komal, M. (2026). The role of smart meter feedback in enhancing inhabitants’ energy efficient behaviour in residential buildings. Energy and Buildings, 351, 116689. https://doi.org/10.1016/j.enbuild.2025.116689
Teixeira, B., Carvalhais, L., Pinto, T., & Vale, Z. (2025). Explainable AI framework for reliable and transparent automated energy management in buildings. Energy and Buildings, 347, 116246. https://doi.org/10.1016/j.enbuild.2025.116246
Zhao, J., & Huh, S.-Y. (2026). What information do residential customers want about their electricity consumption? A choice experiment approach. Energy Policy, 215, 115303. https://doi.org/10.1016/j.enpol.2026.115303

Ma, G. (2026). High School Students’ Evaluation of AI-Generated Energy Recommendations: A Qualitative Study of Trust and Decision-Making. International Journal of Academic Research in Business and Social Sciences, 16(10), 755–766.