Evaluation of Algorithmic Carbon Footprint and Artificial Intelligence (AI) Tools in Environmental Education Programs: Systematic Review

Abstract

The integration of Artificial Intelligence (AI) in educational settings creates pedagogical opportunities while raising environmental concerns regarding data center energy consumption and physical infrastructure footprint. This systematic review synthesizes scientific evidence on the algorithmic carbon footprint of AI tools implemented in environmental education programs. Adhering to the PRISMA 2020 statement and an adapted PICoS framework (Population: educational stakeholders; Phenomenon of interest: AI carbon/energy auditing; Context: environmental/sustainability education; Study design: empirical and theoretical studies), systematic searches were conducted across Scopus, Web of Science, IEEE Xplore, and ERIC up to 6 August 2026. Seven studies met the eligibility criteria: 5 empirical, 1 scoping review, and 1 theoretical-conceptual synthesis. Quality appraisal utilized MMAT (2018 version) for primary empirical studies and an adapted SANRA (2019 version) for non-empirical documents, followed by qualitative narrative synthesis. Results indicate that, within the reviewed corpus (n = 7), studies predominantly examine conversational agents and large language models (LLMs) applied to sustainability education, while highlighting a critical measurement gap regarding the empirical auditing of energy consumption and physical infrastructure during model training and inference. Conceptual frameworks synthesized across non-empirical literature hypothesize a potential misalignment between educational content promoting planetary conservation and the environmental opacity of underlying technological deployments. Key limitations include inferential fragility due to a small, methodologically heterogeneous evidence base restricted to higher education settings. This review was funded by Universidad Estatal Península de Santa Elena (grant Fcm_012) and hosted on the Open Science Framework project repository (DOI: 10.17605/OSF.IO/AD6TM), with its formal frozen protocol registration archived under DOI: 10.17605/OSF.IO/KBWFS.