Purpose: This study aims to investigate how transformative learning has been conceptualised and operationalised in education for sustainable development (ESD) and sustainability learning and to collect evidence on how to support transformative learning in formal and non-formal environments. Design/methodology/approach: The authors conducted a systematic literature review to provide a structured and replicable search and analysis of the relevant literature to produce a bibliometric overview that combines a quantitative description of the body of literature and qualitative analysis of the learning processes, outcomes and conditions. Findings: The convergence between transformative learning and sustainability has become an emerging field of inquiry, despite the superficial use of transformative learning theory in many studies. By examining the learning process, outcomes and conditions in the core sample of studies, this study demonstrates that transformative learning theory – if carefully studied – can contribute to the design and implementation of educational interventions and assessments of learning towards sustainability. Furthermore, the sustainability context provides an empirical grounding that highlights the fact that social learning, the role of experience and the development of sustainability competencies are inherently part of transformative learning. Originality/value: To date, few attempts have been made to better understand how transformative learning theory has been used in sustainability learning and ESD research. This systematic review allows for a better comprehension of how concepts and mechanisms elucidated in transformative learning theory are operationalised in sustainability learning and ESD research and serves as a source of inspiration for those researchers and practitioners who aims to make sustainability education, teaching and learning more transformative.

Artificial intelligence (AI) holds significant potential to advance Sustainable Development Goals by enabling data-driven insights and optimizations. In this analysis, we review 792 articles that explore AI applications in Sustainable Development Goal-related research. The literature is organized along two dimensions: (1) the disciplinary spectrum, from natural sciences to the humanities, and (2) the focus, distinguishing economic from socioecological content. Deep learning and supervised machine learning were the most prominently applied algorithms for forecasting and system optimization. However, we identify a critical gap: only a few studies combine advanced AI applications with deep sustainability expertise. Sustainability needs to strike a balance between contextualization and generalizability to provide tangible knowledge that will lead to responsible change. AI must play a central role in this process. While expectations for AI’s transformative role in sustainable development are high, its full potential remains to be realized.

There are still several gaps to bridge in learning and sustainability transition research, one of which is the empirical exploration of learning processes and outcomes involved in the formation and development of sustainability niches. The purpose of this study is to investigate the formation and development of a sustainability niche through the lenses of individual and social transformative learning, specifically the learning processes and outcomes. We conducted a qualitative exploratory multi-case study of six different projects participating in the sustainable fashion and textiles niche in Mexico. We used documentation and individual interviews (n = 7) to collect evidence of the learning process in the formation and development of this niche. We used transformative learning theory to guide the analysis of the learning phenomena occurring in the development of this niche, distinguishing three main phases where individual learning takes place in the first two and social learning in the third. Analytical elements such as prior learning, disorienting dilemmas, action engagement and network building figure prominently in the learning process and outcomes in the development of the sustainable fashion niche in Mexico. This work provides a better and more detailed understanding of the multi-level perspective model through exploring sub-levels within the niche level, by shedding light on the importance of individual and social learning in the development of sustainable niches.