Green technologies bring together ideas from energy, engineering, environmental science, data analysis and business. For students, that combination can be difficult to navigate. Generative artificial intelligence can make some of these topics easier to explore, but only when it is used as a learning aid rather than treated as an unquestionable source of answers.
This matters for the green and digital transition because future professionals need more than technical knowledge. They must be able to ask good questions, evaluate evidence and apply knowledge to unfamiliar problems. Used carefully, generative AI can help students practise all three.
Making complex ideas easier to explore
A student learning about smart grids, heat pumps or circular production may come across to unfamiliar terminology before understanding how the system works. A generative AI tool such as Chat GPT, Claude, Gemini, Copilot can explain a concept at different levels, compare two technologies or provide a simple example. A student might ask for an explanation of demand response for a secondary-school audience and then request a more technical version with the main variables and assumptions.
This flexibility can support students with different academic backgrounds. It is especially useful in interdisciplinary courses, where an engineering student may need help with a business concept and a management student may need a clear introduction to energy systems. The teacher still defines the learning outcomes and selects reliable sources.
Learning with practical scenarios
Generative AI can also create practice situations. Students could compare options for reducing energy use in a university building, propose indicators for a smart-city project or examine how a small company might introduce a circular business model. The first AI-generated scenario does not have to be perfect. Its value may lie in giving students something concrete to question and improve.
Teachers can design activities in which students identify missing data, challenge assumptions and justify a better solution. For example, an AI tool may recommend solar panels without considering the building, location, budget or electricity demand. Students then have to determine what information is needed before a responsible recommendation can be made. That exercise combines digital literacy with systems thinking and sustainability knowledge.
Teaching students to verify AI output
AI tools can produce convincing explanations that contain errors, outdated information or invented references. For that reason, verification must be part of the task. Students should check important claims against course materials, official datasets, technical documentation and credible research. They should also state when and how they used an AI tool.
A useful classroom routine is simple: ask, check, improve and explain. Students first formulate a clear prompt. They then verify the response, correct its weaknesses and explain why their final answer is stronger. Assessment should reward the quality of reasoning and evidence, not the fluency of the generated text.
Considering the environmental cost
The use of generative AI also raises a sustainability question. Training and operating large AI systems requires computing infrastructure, electricity and water. Exact impacts vary between systems and locations, and they are often difficult for users to see. A course on green technologies should therefore encourage purposeful use instead of automatic use.
Students can ask whether an AI task adds educational value, whether a smaller digital tool would be sufficient and how digital services affect energy demand. This makes AI itself a case study in the relationship between technological innovation and environmental responsibility.
A question for readers
Generative AI can support learning when it helps students explore, practise and question. It becomes less useful when it replaces reading, evidence and independent judgment. Universities can respond by teaching students how to work with AI transparently and critically while keeping sustainability in view.
How could you use generative AI to investigate a green technology without allowing the tool to do the thinking for you? Explore GreenTech Horizons to learn how green, digital and business competencies can develop together.
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Ivana Ninčević Pašalić, Assistant Professor, Faculty of Economics, Business and Tourism, University of Split.
Ivana is an Assistant Professor of Business Informatics at the Faculty of Economics, Business and Tourism, University of Split. Her academic and project work focuses on digital and green transformation, smart cities, information systems and the development of future-oriented competencies. Within GreenTech Horizons, she contributes to connecting higher education with the digital, green and entrepreneurial skills needed for sustainable innovation.
