Artificial Intelligence (AI) presents promising opportunities to enhance experiential learning, but successful integration faces complex obstacles that require careful navigation. This systematic review followed the PRISMA guidelines to synthesise findings from 85 peer‐reviewed studies published in Scopus‐indexed journals on the application of AI technologies in experiential higher education across diverse domains. Results indicate that AI‐based tools such as intelligent tutoring systems, immersive simulations, and virtual reality environments can increase student engagement, motivation, self‐efficacy, and learning outcomes by providing interactive platforms aligned with experiential learning principles. However, findings also highlight significant challenges in effectively leveraging AI to complement rather than replace holistic, human‐centred educational experiences. Many current AI applications lack customisation for diverse learning styles and student backgrounds. Further research is critically needed on adaptive AI systems capable of providing personalised support tailored to individual learners' needs. Substantial teacher training and curriculum redesign are essential to capitalise fully on AI capabilities while delineating appropriate teacher versus technology roles. Realising the immense but complex potential of AI in experiential learning requires nuanced approaches that attend holistically to pedagogical, technical, ethical, and change management dimensions. Further research should target flexible human‐AI collaboration supporting personalised experiential learning, rooted in adaptive systems design and ethical frameworks appropriate to educational contexts.
Nelson Iván Bedoya, Andrés Chiappe, Julio Durand· European Journal of Educatio...· 0 citations
This scoping review examines the growing intersection between lifelong learning (LLL) and artificial intelligence (AI), focusing on whether the promises of assistance, personalization, and automation are aligned with the broader educational, social, and equity-oriented aims of LLL. The review addresses a central concern: although AI is increasingly promoted as a transformative resource for lifelong education, its deployment may not always correspond to the paradigms, geographical contexts, and vulnerability profiles emphasised in LLL literature.
A scoping review was conducted using systematic search and reporting procedures informed by PRISMA 2020 and PRISMA-ScR. Two complementary corpora were analysed: 110 conceptual and empirical articles focused on LLL, and 79 articles addressing the application of AI within LLL contexts. The analysis combined categorical coding with exploratory statistical procedures, including chi-square tests and Cramér's V, to examine descriptive associations between LLL paradigms, geographical regions, vulnerability profiles, and the three dominant promises of AI.
The findings show that LLL paradigms display descriptive variation across regions and are associated with different vulnerability configurations, including socio-economic, territorial, demographic, and human-diversity-related dimensions. In contrast, AI-related articles show weak descriptive associations with LLL paradigms, geographical contexts, and vulnerability profiles. Although personalization appears as a prominent AI promise, assistance and automation are also present across the corpus, usually without strong conceptual alignment with the equity-oriented and socially grounded concerns of LLL.
The review identifies a misalignment between the theoretical and policy-oriented framing of LLL and the practical deployment of AI in educational contexts. This suggests the need to move beyond technology-centred approaches and to design AI-supported lifelong learning initiatives that are explicitly connected to regional needs, learner vulnerability, social justice, and human development. The review contributes an analytical framework for examining how AI promises can be critically aligned with inclusive and transformative visions of lifelong learning.
