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Purpose Advancements in artificial intelligence (AI) have significant potential for university energy management in Palestine, but effective integration of AI in campus sustainability programs will depend on the willingness of lecturers to use AI. In this research, Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Diffusion of Innovations (DoI), and Resource-Based View (RBV) have been adopted to investigate mediator role of AI adoption intention in AI use. Besides, AI use is situated in harmony with international frameworks for sustainability, such as the UN Sustainable Development Goals (SDGs), the Talloires Declaration, and Agenda 2030, with a view to AI contribution towards increased efficiency in terms of energy in universities. Design/methodology/approach A quantitative approach was adopted, with a cross-sectional survey of 269 university lecturers in two universities in Palestine, with simple random selection . Partial Least Squares Structural Equation Modeling (PLS-SEM) examined technological readiness, perceived benefits, financial constraint, awareness, and AI intention for use in a study. Validated scales for measurement assured measurement reliability, and mediating effects examined in terms of testing for routes towards AI use in energy management. Findings The results reveal that technological readiness, perceived benefits, financial constraints, and awareness have a significant impact on AI adoption for energy management in terms of lecturers' intention to use AI in future implementations. Perceived benefits and awareness played a most significant role. Adoption intention was discovered to act as a mediator between these factors and future AI use, supporting investments in AI infrastructure, overcoming financial barriers, and awareness programs in institutions. Originality/value This study provides a novel contribution by integrating multiple theoretical frameworks to understand AI adoption for energy management in Palestinian universities. It highlights AI’s role in advancing sustainability in higher education and offers insights for educators, administrators, and policymakers in fostering AI-driven energy efficiency initiatives.
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