GENIE Learn Project Summary

Smart Learning Environments (SLEs) are Technology-Enhanced Learning systems conceived to provide personalized and context-aware support to learners, as well as to help teachers in the process of designing effective scenarios (Learning Design) that incorporate the knowledge obtained from the analysis of learning data (Learning Analytics). SLEs have demonstrated their capabilities supporting challenging educational settings such as those studied under the umbrella of Hybrid Learning (HL). HL defines novel models of teaching/learning based on the blurring of traditional approaches along different dimensions or dichotomies: learning in physical/digital spaces, face-to-face/online learning, individual/collaborative active learning, etc. For example, writing a collaborative essay about a cultural heritage site, with some of the students working at the classroom or at home while interacting with other students visiting the site itself, represents a HL scenario blurring the physical/digital and individual/collaborative boundaries.

However, state-of-the-art SLEs still suffer from significant limitations that affect learners, teachers and academic managers in HL, incl.: 1) limited types of automatically generated interventions; 2) student models mostly based on quantitative/structured data sources; 3) inflexibility of current conversational interfaces; 4) limited analytics-informed support in learning design, 5) lack of support to adapt educational resources to universal design for learning principles, 6) high teaching orchestration load; 7) scarce use of large-scale analysis of SLE data to support decision making in academic management.

 The starting hypothesis of this coordinated project is that the integration of so-called Generative Artificial Intelligence (GenAI) tools into SLEs can help address the limitations described above for HL. GenAI is the branch of AI aimed at creating realistic content based on a given input or prompt. GenAI is having disruptive effects in educational technology that call for reflective research efforts in order to better understand its affordances and dangers. Indeed, the potential lack of alignment of GenAI with human values for learning, the challenges to current forms of Human-AI collaboration with effects on human agency, and the scarce research evidence about the expected benefits of GenAI for education are some of the risks pointed out by policy makers and the research community.

Therefore, the main goal of the GENIELearn coordinated project is to improve SLEs for HL support by integrating GenAI tools in a way that is aligned with the preferences and values of human stakeholders. Applying human-centered and value-sensitive design principles, the project will propose: 1) a research framework with representative GenAI-enabled HL scenarios, a pedagogical model, and human-AI collaboration models  for the project; 2) a set of GenAI-enhanced solutions for learners, teachers and academic managers to improve learning, learning design and academic decision making in the context of HL; 3) a technological framework with the definition and implementation of an integration infrastructure for a novel GenAI-enhanced SLE that overcomes current limitations, 4) pilot experiences in authentic settings aimed at evaluating the contributions of the project, as well as at enlarging the empirical base about GenAI in education.