Giulia Cosentino

• Anton, Jacqueline; Cosentino, Giulia; Gelsomini, Mirko; Kshitij, Kshitij; Giannakos, Michail & Abrahamson, Dor (2026). Towards minding the disability achievement gap: the case of technology-enhanced inclusive embodied design for integer arithmetic. Educational Technology Research and Development. ISSN 1042-1629. doi: 10.1007/s11423-026-10704-0. Fulltekst i vitenarkiv
• Cosentino, Giulia; Gelsomini, Mirko; Kshitij, Kshitij & Giannakos, Michail (2025). Students’ experience and learning outcomes in multisensory environments: the moderating role of interaction modalities. Smart Learning Environments. 12(1), s. 1–28. doi: 10.1186/s40561-025-00402-4. Fulltekst i vitenarkiv
• Anton, Jacqueline; Cosentino, Giulia; Gelsomini, Mirko; Sharma, Kshitij; Giannakos, Michail & Abrahamson, Dor (2024). Mathematics MOVES Me—Digital Solutions for Co-ordinating Enactive and Symbolic Resources: The Case of Positive and Negative Integer Arithmetic. Digital Experiences in Mathematics Education. ISSN 2199-3246. 11(2), s. 275–275. doi: 10.1007/s40751-024-00158-5. Fulltekst i vitenarkiv Vis sammendrag

  We present an innovative educational design for basic arithmetic that responds to students’ documented difficulties with adding and subtracting single-digit positive and negative numbers. The design utilizes MOVES, a technological architecture that combines floor- and wall-projected interactive interfaces. Students enact arithmetic operations, e.g., “3 – (-2)” by walking along a projected body-scale number line, while their actions are captured and analyzed to provide in-the-moment feedback on elements of their solution procedure. Next, a screen-based avatar is introduced who mimics their whole-body movements. Finally, analogous problems are presented on a tablet that utilizes tangible interaction, where seated students walk the avatar, now as an action-figure, along a standard-sized number line. Our theoretical framework, design conjecture, product evaluation, and data analysis all pertain to fostering conceptual understanding through co-ordinating full-body egocentric experiences on a body-scale number line with the allocentric experience of “puppeting” the avatar along the desk-scale number line. We introduce the educational design and report on its pilot trials. Based on these preliminary results, we speculate on the nature and type of supports that students require to co-ordinate these perspectives and discuss implications for future iterations of the design

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• Cosentino, Giulia; Giannakos, Michail; Divitini, Monica & Kshitij, Kshitij (2025). Embodied Multisensory Interactions and AI as a Means to amplify Children’s Learning experiences. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232691531. Fulltekst i vitenarkiv Vis sammendrag

  The integration of multisensory environments (MSEs) in educational settings presents new opportunities for enhancing children’s learning experiences through embodied, interactive approaches. Multisensory learning, supported by advanced sensing technologies, enables richer engagement by combining different stimuli such as, visual, motion, touch, and auditory, fostering deeper cognitive and affective learning processes. At the intersection of Child-Computer Interaction (CCI), Learning Analytics (LA), and Artificial Intelligence (AI), this research explores how MSEs can be designed and evaluated to optimize interaction modalities, support learning analytics, and enhance hybrid intelligence (HI) collaborations. This thesis investigates the design, implementation, and impact of AI-enhanced MSEs, leveraging LA to capture and interpret children’s embodied interactions. Through a Design-Based Research (DBR) approach, this work systematically explores how different interaction modalities affect children learning experiences. By analyzing multimodal data, including eye tracking, motion analysis, and physiological data, this research identifies the benefits, challenges, and trade-offs associated with these modalities in adaptive learning environments. This research highlights the role of teachers, in shaping the effectiveness of learning systems, emphasizing the need for including them in the design process. Empirically, this research is grounded in the design and evaluation of MOVES, a portable multisensory system that facilitates embodied learning through sensor-driven interactions. The findings demonstrate how motion-based learning can enhance conceptual understanding, particularly in mathematics education, by enabling body-scale interactions. Through experimental studies, this work provides insights into how AI-enhanced MSEs can be effectively integrated into learning contexts, offering design recommendations for future MSEs for children learning. By embedding AI in MSEs, this work contributes to the development of HI models, where AI augments, rather than replaces, human expertise. While this research makes significant effort in bridging AI, LA, and embodied learning, it also identifies open challenges and areas for future investigation. These include refining AI-driven feedback mechanisms, expanding accessibility considerations, and further exploring the balance between human and AI in learning environments. Ultimately, this thesis contributes to the growing body of knowledge in CCI, AI-driven education, and embodied learning, advancing both theoretical understanding and practical applications in learning technologies.

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