Michael Lodi

Background/Objectives: The aging global population has led to a rising prevalence of type 2 diabetes mellitus (T2DM), in which biomedical outcomes alone fail to capture patients’ lived experiences. Patient-Reported Outcome Measures (PROMs) can provide insights into psychological, psychosocial, and quality-of-life (QoL) dimensions, yet their use—particularly among older adults—remains inconsistent. This AI-assisted rapid review aimed to map how PROMs are currently applied in adults with T2DM, with specific attention to studies involving older populations, focusing on their role in assessing well-being, distress, depression, and treatment satisfaction, as well as their implementation in clinical and organizational practice. Methods: A rapid review was conducted using Elicit, an AI tool designed to support systematic evidence synthesis. Studies published between 2015 and 2025 were identified from Semantic Scholar, complemented by manual searches for recent or unindexed papers. Eligibility criteria required inclusion of adults with T2DM and use of validated PROMs in real-world settings. Studies explicitly describing older or elderly subgroups were highlighted separately. After screening 504 records, 167 studies were included. Data extraction covered study design, instruments used, populations, outcomes, and implementation details. Results: The most frequently assessed outcomes were diabetes distress, depression, QoL, treatment satisfaction, and self-efficacy. Common instruments included PAID, DDS, PHQ-9, WHO-5, EQ-5D, SF-36, DTSQ, and GDS. Evidence showed PROMs effectively identified high-risk patients and informed tailored interventions, but integration into routine care remained limited. Barriers included workflow disruption, lack of provider training, heterogeneity of tools, and insufficient cultural validation. Facilitators included brief instruments, digital administration, and linkage with care planning. Conclusions: PROMs are valuable in capturing psychosocial and psychological burdens in adults with T2DM, including but not limited to older populations, but routine implementation is inconsistent. Broader adoption will require digital infrastructure, clinician training, and organizational integration, as well as the development of PROMs that capture experiences with emerging diabetes technologies. Methodologically, this study illustrates the feasibility of AI-assisted rapid reviews to generate timely, evidence-informed syntheses.

Background/Objectives: The aging global population has led to rising prevalence of type 2 diabetes mellitus (T2DM), in which biomedical outcomes alone fail to capture patients’ lived experiences. Patient-Reported Outcome Measures (PROMs) can provide insights into psychological, psychosocial, and quality-of-life (QoL) dimensions, yet their use in older adults remains inconsistent. This AI-assisted rapid review aimed to map the current use of PROMs in older adults with T2DM, focusing on their role in assessing well-being, dis-tress, depression, and treatment satisfaction, as well as their implementation in clinical and organizational practice. Methods: A rapid review was conducted using Elicit, an AI tool designed to support sys-tematic evidence synthesis. Studies published between 2015 and 2025 were identified from Semantic Scholar and additional manual searches. Eligibility criteria required inclu-sion of older adults with T2DM and use of validated PROMs in real-world settings. After screening 504 records, 167 studies were included. Data extraction covered study design, instruments used, populations, outcomes, and implementation details. Results: The most frequently assessed outcomes were diabetes distress, depression, QoL, treatment satisfaction, and self-efficacy. Common instruments included PAID, DDS, PHQ-9, WHO-5, EQ-5D, SF-36, DTSQ, and GDS. Evidence showed PROMs effectively identified high-risk patients and informed tailored interventions, but integration into rou-tine care remained limited. Barriers included workflow disruption, lack of provider train-ing, heterogeneity of tools, and insufficient cultural validation. Facilitators included brief instruments, digital administration, and linkage with care planning. Conclusions: PROMs are valuable in capturing psychosocial and psychological burdens in older adults with T2DM, but routine implementation is inconsistent. Broader adoption will require digital infrastructure, clinician training, and organizational integration. Methodologically, this study illustrates the feasibility of AI-assisted rapid reviews to gen-erate timely, evidence-informed syntheses.

We present a learning path on cryptography for primary school students (Grade 5), which we designed and tested. The project aims to raise initial awareness of the core ideas of modern cryptography, which are fundamental concepts for becoming informed and active citizens in modern digital society. For this reason, we designed a progression of unplugged activities (sometimes integrated with task-specific, block-based programming environments) to expose students to different cryptographic techniques, where each new activity is motivated by an analysis of the criticalities encountered previously. We used unplugged activities to show that Computer Science (CS) does not necessarily imply the use of digital devices and to allow students to focus on its general scientific principles. We describe the designed learning module, discuss the main hinges in the experiment, and reflect on the lessons learned. The final evaluation showed excellent learning outcomes and high satisfaction with the activities. As cryptography uses mathematical tools (e.g., modular arithmetic, statistics), some parts of which are within reach of primary school children but rarely taught to them, it was possible to dwell on these aspects, making them experience mathematical objects in a non-standard context, and also stimulating a greater awareness of the impact of mathematics and CS in everyday life.

In a historical moment in which Artificial Intelligence and machine learning have become within everyone’s reach, science education needs to find new ways to foster “AI literacy.” Since the AI revolution is not only a matter of having introduced extremely performant tools but has been determining a radical change in how we conceive and produce knowledge, not only technical skills are needed but instruments to engage, cognitively, and culturally, with the epistemological challenges that this revolution poses. In this paper, we argue that epistemic insights can be introduced in AI teaching to highlight the differences between three paradigms: the imperative procedural, the declarative logic, and the machine learning based on neural networks (in particular, deep learning). To do this, we analyze a teaching-learning activity designed and implemented within a module on AI for upper secondary school students in which the game of tic-tac-toe is addressed from these three alternative perspectives. We show how the epistemic issues of opacity, uncertainty, and emergence, which the philosophical literature highlights as characterizing the novelty of deep learning with respect to other approaches, allow us to build the scaffolding for establishing a dialogue between the three different paradigms.

Introductory programming courses (CS1) are difficult for novices. Inspired by Problem solving followed by instruction and Productive Failure approaches, we define an original “necessity-driven” learning design. Students are put in an apparently well-known situation, but this time they miss an essential ingredient (the target concept) to solve the problem. Then, struggling to solve it, they experience the necessity of that concept. A direct instruction phase follows. Finally, students return to the problem with the necessary knowledge to solve it. In a typical CS1 learning path, we recognise a challenging “rollercoaster of abstraction”. We provide examples of learning sequences designed with our approach to support students when the abstraction changes (both upward and downward) inside the programming language, for example, when a new construct (and the related syntactical, conceptual, and strategic knowledge) is introduced. Also, we discuss the benefits of our design in light of Informatics education literature.

The pervasiveness of Computer Science (CS) in today’s digital society and the extensive use of computational methods in other sciences call for its introduction in the school curriculum. Hence, Computer Science Education is becoming more and more relevant. In CS K-12 education, computational thinking (CT) is one of the abused buzzwords: different stakeholders (media, educators, politicians) give it different meanings, some more oriented to CS, others more linked to its interdisciplinary value. The expression was introduced by two leading researchers, Jeannette Wing (in 2006) and Seymour Papert (much early, in 1980), each of them stressing different aspects of a common theme. This paper will use a historical approach to review, discuss, and put in context these first two educational and epistemological approaches to CT. We will relate them to today’s context and evaluate what aspects are still relevant for CS K-12 education. Of the two, particular interest is devoted to “Papert’s CT,” which is the lesser-known and the lesser-studied. We will conclude that “Wing’s CT” and “Papert’s CT,” when correctly understood, are both relevant to today’s computer science education. From Wing, we should retain computer science’s centrality, CT being the (scientific and cultural) substratum of the technical competencies. Under this interpretation, CT is a lens and a set of categories for understanding the algorithmic fabric of today’s world. From Papert, we should retain the constructionist idea that only a social and affective involvement of students into the technical content will make programming an interdisciplinary tool for learning (also) other disciplines. We will also discuss the often quoted (and often unverified) claim that CT automatically “transfers” to other broad 21st century skills. Our analysis will be relevant for educators and scholars to recognize and avoid misconceptions and build on the two core roots of CT.