AI-Enhanced Green Decision-Making

Environmental and sustainability awareness

Description

Introduction

Many professional decisions in STEM fields have environmental impacts. Material selection, equipment or infrastructure upgrades, production planning, the installation of energy-efficient systems, and the selection of software infrastructure can have varying impacts on resource use, emissions, waste, water, biodiversity, and human health. Therefore, green decision-making is not a specialized field limited to a specific discipline; it is a common way of thinking that STEM graduates will apply in various professional contexts.

This section addresses environmental awareness as the ability to understand environmental systems and the impacts of human activities; to interpret these impacts in terms of magnitude, duration, and life cycle; and to relate claims to reliable indicators and sources. Environmental awareness enables students to recognize an environmental issue and understand what information is needed to explain it. The green decision-making framework to be introduced at the introductory level encompasses the skills of recognizing the environmental and sustainability dimensions of a decision, identifying its limitations and the necessary evidence, and defining the role of AI support. Subsequent subtopics will delve into evidence analysis, resource and energy efficiency, policy, ethics, and systems thinking.

Evidence-based reasoning and decision making

Description

Introduction

Evidence-based reasoning and decision-making are critical components of professional development programmes, particularly for university teaching staff who aim to enhance their teaching and research practices. This approach involves the systematic use of the best available evidence to inform decisions, thereby improving outcomes in various professional contexts. The integration of evidence-based reasoning into professional development can enhance critical thinking, reduce biases, and promote more effective decision-making processes (Kurien et al., 2022; Grawitch et al., 2025).

In the AIGUIDE context, evidence-based reasoning is combined with AI literacy so that STEM teaching staff can collect, prepare, interpret and analyse data or evidence for green decision-making tasks, and so that their students can, in turn, justify green decisions with evidence. This subtopic therefore addresses two intertwined layers: (1) the logic and process of evidence-based reasoning and decision-making, and (2) the AI literacy components — data reliability and variable identification, argumentation, and AI-supported data interpretation — that make this process practical and scalable in the classroom.

Resource and energy efficiency focused decisions

Description

Introduction

Universities, laboratories and engineering workplaces constantly face decisions about how to use limited resources: Which equipment should be replaced? How should heating, cooling and computing loads be scheduled? Which of several technically feasible options is the most efficient and sustainable? These are rarely single-criterion choices. Energy consumption, purchase and operating cost, performance, lifetime, maintainability and environmental impact pull in different directions, and a defensible decision requires making these trade-offs explicit.

This subtopic builds on the evidence-based reasoning skills developed in Subtopic 3.2 and moves one step further: from interpreting data to comparing decision alternatives. The teacher learning outcome is to compare resource-efficiency and energy-related decision alternatives using AI-supported analysis; the student transfer outcome is to decide on resources according to efficiency and sustainability criteria. Three AI literacy components support these outcomes: AI-supported energy forecasting, resource-use analysis and trade-off analysis.

Throughout the session, participants work with tools that are realistic for classroom use — Orange Data Mining, the AI features of Excel/Google Sheets and freely available energy calculators — so that what is learned in the professional development session can be transferred directly to their own STEM courses.

Climate policy and institutional decisions

Description

Introduction

Universities, ministries, municipalities and companies increasingly make green decisions inside institutional structures: emission-reduction targets, sustainability strategies, procurement rules and climate action plans. For STEM teaching staff, this subtopic addresses an advanced layer of AI literacy: how to interpret climate or institutional AI-supported green decision-making scenarios, and how to help students evaluate climate-related uncertainties and their implications. Whereas earlier subtopics focused on evidence-based reasoning (3.2) and resource-efficiency trade-offs (3.3), Subtopic 3.4 moves to the policy level: how institutions transform evidence into decisions, why apparently rational policies fail or succeed in different institutional contexts, and how risk analysis structures policy-based decision making.

This session combines three strands drawn from the module matrix: (1) policy-relevant evidence and institutional data interpretation as the AI literacy component, (2) risk analysis and policy-based decision making as the decision-making skill, and (3) hands-on work with climate data tools, Power BI Copilot and chatbots as the supporting AI toolset. The material below can be used directly with teaching staff in a 60-minute professional development session, and the same content and activities can then be transferred to students in STEM lectures.

Ethics, equity and multi-stakeholder perspectives

Description

Introduction

This 60-minute professional development session supports STEM teaching staff in integrating ethics, equity and multi-stakeholder perspectives into AI-supported green decision tasks. Earlier subtopics of Module 3 focused on evidence-based reasoning and on resource- and energy-efficiency analysis; this subtopic adds the human dimension. Even a technically optimal green alternative can fail — or cause harm — if the values, rights and lived circumstances of affected groups are ignored. The session therefore develops two intertwined competences defined for Subtopic 3.5: an AI literacy component (responsible AI and the ethical implications of AI in green decisions) and a decision-making skill set (moral and ethical reasoning and perspective taking).

The intended teacher learning outcome is to integrate multiple perspectives and ethical reasoning into AI-supported green decision tasks. The intended student transfer outcome is that students know the stakeholder impacts involved in evaluating green alternatives. The estimated duration is 60 minutes and the targeted AI literacy level is medium.

Systems and interdisciplinary thinking

Description

Introduction

Complex green challenges — climate change, resource depletion, biodiversity loss, energy transitions — rarely respect disciplinary boundaries. A decision about fertiliser policy affects drinking water quality and aquatic biodiversity; a decision about campus energy systems affects budgets, laboratory schedules and emissions. Interdisciplinary systems thinking is therefore increasingly recognised as an essential approach for addressing such multifaceted problems, because it integrates diverse disciplinary perspectives and fosters holistic understanding (Barile & Saviano, 2021; Voulvoulis et al., 2022).

For STEM teaching staff, this subtopic has a dual purpose. First, it develops your own capacity to use AI-supported systems for connecting environmental, technological, social and economic variables — the teacher learning outcome for Subtopic 3.6. Second, it prepares you to design learning experiences in which your students analyse green problems by using systems thinking — the student transfer outcome. The AI literacy components addressed are AI-assisted systems mapping and interdisciplinary evidence integration, at a medium AI literacy level, supported by tools such as systems mapping tools, Miro AI, chatbots and climate data tools.

Sector-specific and discipline-specific applications

Description

Introduction

Green decision-making in STEM (Science, Technology, Engineering, and Mathematics) fields such as engineering, energy, transportation, manufacturing, agriculture, environmental sciences, and mathematical modeling shares the same fundamental approach. This involves understanding environmental impacts, obtaining evidence to support decisions, and explaining the rationale for the decision in a verifiable manner. However, selecting a building reinforcement method, for example, is not evaluated using the same type of evidence or professional approach as selecting a renewable energy source, a specific agricultural practice, or an optimization model. Discipline-specificity involves appropriately defining the method of validation, acceptance criteria, and boundaries of responsibility.