Research & Insight

What does meaningful digital engagement really look like? In AI-supported higher education, simply logging in, clicking on resources or using a chatbot does not necessarily mean that a student is meaningfully engaged. Deliverable 5.2 of the HITS Project explores how digital academic engagement can be understood through two complementary dimensions: Digital Academic Commitment and Digital […]

What if effective AI tutoring started by understanding how students actually learn? This fundamental question drives Deliverable 5.1 of the HITS Project, which explores the analytical and strategic foundations of the HITS chatbot, demonstrating how understanding academic engagement patterns can lead to more meaningful and personalized support. From Theory to Profiles: The Foundations of the

How do you build a testing tool that’s fair for every learner – not just accurate on average? We asked the statistics to answer. Deliverable T4.3 of the HITS Project lays out the methodology behind the HITS psychometric tool, built on Item Response Theory (IRT). Instead of relying on a single reliability score, the model

What do students, teachers and tutors actually need from an AI tutoring system? We asked them directly. Deliverable T4.2 of the HITS Project reports on a Participatory Action Research study exploring the factors that promote, or hinder, pro-digital behaviours in higher education. Instead of only measuring technology use, the research listened to the people who

Partners of the HITS Project developed an 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐜𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐢𝐞𝐬 𝐚𝐢𝐦𝐞𝐝 𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐡𝐨𝐰 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐚𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 𝐭𝐨𝐨𝐥𝐬 𝐚𝐫𝐞 𝐮𝐬𝐞𝐝, and what they actually measure, in higher education contexts. Here follows some of the most relevant cases emerged thanks to this research. 𝗖𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝘆 𝗳𝗿𝗼𝗺 𝗩𝗶𝗲𝘁𝗻𝗮𝗺 This study tested a multi-criteria assessment model designed to evaluate digital

𝐖𝐡𝐚𝐭 𝐝𝐨 𝐞𝐝𝐮𝐜𝐚𝐭𝐨𝐫𝐬 𝐚𝐧𝐝 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐜 𝐬𝐭𝐚𝐟𝐟 𝐧𝐞𝐞𝐝 𝐟𝐨𝐫 𝐇𝐈𝐓𝐒 𝐭𝐨 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐡𝐢𝐠𝐡𝐞𝐫 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧? Successful integration of High Intelligent Tutoring Systems (HITS) in academic environments requires clear priorities to ensure effective, sustainable use. Based on recent scientific literature, 𝐂𝐡𝐚𝐩𝐭𝐞𝐫 𝟔 of the HITS WP3 Handbook 𝐬𝐮𝐦𝐦𝐚𝐫𝐢𝐬𝐞𝐬 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐧𝐞𝐞𝐝𝐬 𝐨𝐟 𝐞𝐝𝐮𝐜𝐚𝐭𝐨𝐫𝐬 𝐚𝐧𝐝 𝐚𝐜𝐚𝐝𝐞𝐦𝐢𝐜 𝐬𝐭𝐚𝐟𝐟. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 pathways

As the HITS project continues to explore the integration of Artificial Intelligence in Higher Education, Chapter 5 of our Handbook offers an in-depth look into what students, educators, and mentoring experts really think about Intelligent Tutoring Systems (ITS). Based on participatory research involving over 500 participants from 64 universities across Europe, this chapter brings together

The HITS project continues to delve into the evolving landscape of digital and tutoring services in higher education with the latest output of Work Package 3: the Needs and Prioritisation Map. This activity compiles the results of previous research phases, including participatory surveys conducted across 57 universities in Europe. With input from 253 students, 82

As artificial intelligence (AI) becomes an increasingly influential tool in higher education, the HITS project has set out to address the ethical challenges surrounding its use in tutoring services. By crafting an ethical code, we aim to ensure AI technologies are applied in ways that are secure, inclusive, transparent, and beneficial to the diverse needs

The second edition of “The role of Artificial Intelligence in Higher Education” also took place in Valladolid at the UVAINNOVA Building on the Miguel Campus, attended by experts such as José S. Santamaría from the University of Castilla La Mancha, Andrea Marconi and Fabrizio Fornani from the University of Camerino (Italy), and Paulina Spanu and