Case scenarios from Industry

Real-life examples, cases and scenarios to understand green decision-making

Description

Introduction

Artificial Intelligence (AI) is becoming an important decision-support technology for sustainability. AI is increasingly being applied to sustainability-related problems, including analysing large datasets, predicting environmental outcomes, optimising resource use, and recommending sustainable actions. (Kumar Kar et al., 2022)

However, human responsibility, oversight and accountability in AI-related decision-making remain relevant. (UNESCO, 2022) AI can support professionals by identifying patterns that would be difficult to detect manually. Human expertise remains essential for interpreting AI recommendations, considering ethical implications, and balancing environmental, economic, and social priorities.

For higher education (HE) teachers, real-world cases provide an effective way to demonstrate how AI contributes to solving sustainability challenges. Rather than teaching AI algorithms in isolation, authentic case studies encourage learners to analyse complex situations, evaluate alternative actions, and understand why different decisions may be appropriate under different conditions.

Adapting real-life case scenarios into course contents

Description

Introduction

Artificial Intelligence is increasingly used in environmental and industrial decision-making, from optimising energy consumption and forecasting climate risks to improving resource management and facilitating sustainable production systems. (Uriarte et al., 2024) However, understanding AI's role in green decision-making requires more than knowledge of AI technologies. STEM students need to understand how AI-supported solutions are developed, interpreted, evaluated, and adapted to different real-world situations.

Case-based learning has a long tradition in higher education, particularly in professional disciplines where students must apply knowledge to complex situations. Effective cases encourage learners to analyse realistic problems, consider multiple perspectives, and develop evidence-based solutions. (Herreid, 2007) Moreover, real-world cases can enable deeper understanding of complex phenomena by examining them within their actual context. (Yin, 2018)

When AI-supported green decision cases are introduced into courses, teachers can use them in several ways such as: as an introduction to a topic, where a real industrial example can motivate students before introducing theoretical concepts; as an application activity, where students use existing knowledge to analyse how AI supports environmental decisions; as a comparison framework, where students examine why the same AI approach may produce different outcomes in different contexts; and as a reflection tool, where students evaluate the relationship between AI recommendations and human judgement. The objective is not to teach students to accept AI-generated decisions, but to develop their ability to critically evaluate AI as a decision-support technology.

Designing case-based green decision tasks

Description

Introduction

Case-based learning has become an established instructional approach for developing higher-order thinking skills because it places learners in authentic situations where they must analyse evidence, evaluate alternatives, and justify decisions rather than simply recall factual knowledge. Unlike traditional teacher-centred instruction, case-based learning encourages active engagement with realistic problems, promoting critical thinking, problem-solving, and contextual reasoning. In sustainability education, this method is especially valuable because environmental challenges are often complex, multidisciplinary, and characterised by uncertainty, requiring learners to balance environmental, economic, and social aspects when making decisions (Herreid, 2011; UNESCO, 2020).

The application of artificial intelligence into case-based learning further enhances these learning experiences by enabling students to explore how AI-supported systems help environmental decision-making. AI technologies can process large and diverse datasets, observe patterns, generate predictions, and recommend potential solutions in areas such as climate adaptation, energy management, precision agriculture, biodiversity conservation, and resource efficiency. However, AI outputs should be interpreted critically rather than accepted uncritically, as their reliability depends on data quality, model assumptions, and contextual factors. Consequently, educational activities should position AI as a decision-support tool that complements, rather than replaces, human judgement and ethical reasoning, while maintaining appropriate human oversight, accountability and awareness of AI-related uncertainties (UNESCO, 2021; OECD, 2024a; OECD, 2024b).

Designing effective green decision cases requires authentic scenarios, reliable evidence, and carefully structured opportunities for students to compare alternative solutions. Well-designed cases encourage learners to identify relevant information, recognise uncertainties, evaluate trade-offs, and justify their recommendations using transparent reasoning. This process reflects the principles of Education for Sustainable Development which aims to develop competencies such as systems thinking, critical reflection, collaborative problem-solving, and responsible decision-making, enabling learners to address complex sustainability challenges in diverse contexts (UNESCO, 2017; Wiek, Withycombe & Redman, 2011).

This subtopic introduces practical guidance for designing AI-supported, case-based green decision tasks that foster analytical thinking and contextual judgement. By engaging with authentic sustainability cases, students learn to examine environmental problems from multiple perspectives, critically evaluate AI-generated recommendations, and assess whether proposed solutions can be transferred or adapted to different situations. Such learning experiences help prepare students to make informed, evidence-based decisions in an increasingly data-rich and AI-enabled society.