Due to the recent rapid development of artificial intelligence (AI) and its expanding impact on the planet, green and sustainable AI research has increasingly gained attention. This systematic literature review searches main databases, including Scopus, Web of Science, and Google Scholar, using an organized methodological approach. Following a thorough screening process, 49 final studies published between 2016 and 2026 are selected from an initial identification of 325 original records. We identify and analyze four key categories of sustainable AI practices: (1) model-level algorithmic efficiency, (2) hardware- and system-level optimization, (3) lifecycle- and data-centric approaches, and (4) operational and policy-level sustainability. We also highlight and explain four dimensions at the intersection of AI and environmentally responsible behavior: AI for sustainable applications’ development in industries, ethical considerations and accountability in using AI, and opportunities enabled by generative AI. We then combine existing taxonomies, evaluation metrics, and challenges to identify areas for improvement and suggest future research directions. Based on our analysis, we emphasize the need for interdisciplinary cooperation to facilitate responsible AI innovation and match it with global sustainable development goals (SDGs). We also highlight the importance of developing adequate frameworks along with precisely defined and standardized metrics to assess the environmental impact of AI. This review aims to encourage more responsible and environmentally friendly AI practices by providing a structured framework for researchers, educators, and professionals engaged in sustainable AI.

Academic advising is fundamental to student success, yet the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming the delivery of academic support. While predictive models and Recommendation Systems (RS) are becoming more accessible, the existing literature remains fragmented across diverse technical architectures and institutional objectives, preventing a clear understanding of the field’s evolution. In view of this, we present a Systematic Literature Review (SLR) of AI-driven academic advising, adhering to the PRISMA 2020 framework. We analyzed 27 peer-reviewed studies published between 2018 and 2025 to synthesize methodological trends and functional applications. Our findings reveal that while most systems prioritize pathway recommendations via classical ML or hybrid architectures, Early-Warning Systems (EWS) remain anchored in predictive classification. Furthermore, a nascent shift toward Generative AI (GenAI) indicates a move toward more interactive advising, though transparency and evaluation standards remain inconsistent. This review identifies a critical tension between algorithmic performance and institutional interpretability. We conclude by proposing a research agenda that emphasizes the need for cross-context validation and the development of socio-technical frameworks that integrate AI into existing higher education management structures.

This observational and exploratory study describes the agreement between peer assessments and professor assessments in two successive peer assessment workshops involving 96 second-year engineering students at the National School of Applied Sciences of Kenitra, part of Ibn Tofail University in Morocco. Equal-sized groups were formed based on student profiles using clustering and a balancing system. The first strategy aimed to maximise within-group profile similarity to form homogeneous groups by placing students from the same cluster in the same group, while the second aimed to maximise profile diversity to form heterogeneous groups by combining students from different clusters, subject to the available cluster sizes. For each workshop, we compared the average peer grade for each assignment with the professor’s grade using Pearson’s correlation, TOST equivalence tests, and Bland–Altman analysis. In the full samples, the first workshop showed a strong correlation (r = 0.90) and equivalence of means within the predefined ±4-point margin, whereas the second showed a weaker correlation (r = 0.43), a more negative mean bias, wider limits of agreement, and no equivalence of means within this margin. The ±4-point criterion relates to the mean difference and does not imply that acceptable agreement is established for every assignment. A sensitivity analysis restricted to assignments that received exactly four peer ratings supported the equivalence of means observed in both workshops, with mean differences of −0.2 and −0.4 points, respectively, whereas differences in correlation and dispersion persisted. The grouping strategy varied in conjunction with the task, the assessment grid, participation and the number of peer ratings, so its independent contribution cannot be determined. The inferential analyses did not account for dependence arising from shared assessors and groups, making the confidence intervals and conclusions regarding equivalence provisional. Controlled comparisons with simpler grouping methods are needed to determine whether profile-based grouping contributes to the quality of peer assessment.