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GenAI in Teaching, Learning, and Assessment for Enhancing Students ’Digital Competences in STEMI Education (GNUKS)

GenAI in Teaching, Learning, and Assessment for Enhancing Students ’Digital Competences in STEMI Education (GNUKS)

Full Project Title: GenAI in Teaching, Learning, and Assessment for Enhancing Students ’Digital Competences in STEMI Education (GNUKS)
Project Code: uniri-mz-25-35
Funding: European Union – NextGenerationEU (UNIRI)
Duration: 4 years (from October 1, 2025 to September 30, 2029)
Project Leader: Assoc. Prof. Gordan Đurović, PhD
Institution: FIDIT, University of Rijeka

The project aims to develop a didactic model for integrating GenAI tools into teaching, learning, and assessment processes, with the goal of enhancing the digital and AI competences of students and teachers.

Research Context

The rapid integration of GenAI tools into society, particularly in education, has brought new opportunities and challenges for higher education institutions. In STEMI programs, where analytical and technical skills are developed, students are increasingly exposed to GenAI tools but still lack clear guidelines on their use in learning and assessment. Although GenAI has the potential to support personalized learning through recommender systems, improve assessment, and facilitate problem-solving, higher education institutions still lack structured approaches for their systematic integration.

The proposed project builds on the research projects “Supporting the Assessment of Knowledge in STEM Fields within the ELARS Recommender System” and “Supporting Personalized Learning in STEM through Educational Personas and Recommendations”. The results of these projects, combined with the development and accessibility of GenAI tools, have opened new avenues for research. Initial studies have demonstrated the high potential of GenAI technologies for enhancing the teaching and learning process and highlighted the need for systematic integration, particularly in STEMI programs and in the development of students ’and teachers ’digital competences.

Research Objectives

The general objective is aligned with the goals of the project team’s previous research, namely to improve the quality of education by introducing innovative pedagogical approaches and computer technologies for e-learning.

The specific objectives of the project focus on strengthening the digital and AI competences of students and teachers through the development of a didactic model for integrating generative artificial intelligence (GenAI) tools into teaching, learning, and assessment processes within STEMI study programs in higher education.

Research Methods

The research will be conducted using the Design-Based Research (DBR) methodology, which enables the iterative development and evaluation of educational innovations in collaboration with teachers and students. The project combines qualitative and quantitative methods, applying the Motivation Model (MM), the Technology Acceptance Model (TAM), and the Value-Based Adoption Model (VBAM). Data will be collected through anonymous surveys and by monitoring the activities of students and teachers, with the aim of analyzing motivation, acceptance, and adoption of GenAI tools within the educational process.

A key element of the methodology will be practical experiments in which students, through coursework, will develop skills in writing effective prompts, managing AI-generated conversations, and using GenAI tools for learning. The project will also explore their application in assessment activities, including responsible use during knowledge checks as well as supporting teachers in evaluating student work.

Organization of Scientific Activities

The proposed project is structured into four work packages, each lasting one year:

  1. Work Package 1: Research and Development of the Didactic Framework – includes literature review, analysis of existing practices, and development of a didactic framework for integrating GenAI tools through DBR cycles.
  2. Work Package 2: Development of the ELARS Module – involves creating a new module for prompt-writing recommendations and testing GenAI support in teaching and assessment in selected courses.
  3. Work Package 3: Full Implementation and Testing – focuses on broader implementation of the designed GenAI activities across multiple courses.
  4. Work Package 4: Final Refinement and Sustainability – includes refining the developed didactic model and creating a handbook with guidelines for teachers.

Throughout all four years of the project, results will be continuously disseminated through conference presentations and publications in scientific journals.

Project Team

  • Assoc. Prof. Gordan Đurović, PhD, Project Leader

  • Assoc. Prof. Martina Holenko Dlab, Ph.D., Associate Member

  • Assist. Prof. Marko Horvat, Ph.D., External Collaborator from FER

  • Marijana Živić Đurović, M.Sc., Associate Member

  • Ivan Tudor, Associate Member

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