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Leonardo Castellani

dblp:429/6862 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%
Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 100%
Artificial intelligence
1 paper
Trustworthy machine learning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › AI education
AI literacy
1.012026
Unplugged Activities on Machine Learning and Their Evaluation Through Mental States Attribution · AAAI 2026
Learning and educational technologies › computing education
AI education for children
1.012026
Unplugged Activities on Machine Learning and Their Evaluation Through Mental States Attribution · AAAI 2026

Methods — techniques the papers use, named apart from their topics

unplugged activities · 3.0mental state attribution · 3.0
YearPublicationVenuePosition
2026 Unplugged Activities on Machine Learning and Their Evaluation Through Mental States Attribution
abstract
Theory of mind refers to the attribution of mental states that humans ascribe to other humans or objects (such as computer-based systems). Recently, the attribution of mental states has been investigated toward Artificial Intelligence (AI) as a basic manner to capture people's engagement toward it, and people's perception about AI social skills and AI capabilities. In line with this idea, mental state attribution can be used as an indirect measure of students' understanding of AI functioning, and in particular of the kind of interactions students may have with AI systems. Too often is the case of people using generative AI systems in ways that exceed their actual ways of functioning. In our study, children of age in the range 9-12 were involved in one-shot unplugged activities concerning data and models. The unplugged activities were not aimed at teaching the theory of Machine Learning, but rather they were designed so as to provide awareness on some basic mechanisms and help developing a correct use of tools that are becoming more and more present in everyday life. This paper introduces the activities and reports the results that were achieved.
Matteo Baldoni, Cristina Baroglio, Monica Bucciarelli, Sara Capecchi, Leonardo Castellani, Elena Gandolfi, Francesco Ianì, Elisa Marengo, Roberto Micalizio
AAAI5