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Arash Zaghi

Publications and source records attributed to Arash Zaghi.

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Integrated Information in Relational Quantum Dynamics (RQD)

We introduce a quantum integrated-information measure $Φ$ for multipartite states within the Relational Quantum Dynamics (RQD) framework. $Φ(ρ)$ is defined as the minimum quantum Jensen-Shannon distance between an n-partite density operator $ρ$ and any product state over a bipartition of its subsystems. We prove that its square-root induces a genuine metric on state space and that $Φ$ is monotonic under all completely positive trace-preserving maps. Restricting the search to bipartitions yields a unique optimal split and a unique closest product state. From this geometric picture we derive a canonical entanglement witness directly tied to $Φ$ and construct an integration dendrogram that reveals the full hierarchical correlation structure of $ρ$. We further show that there always exists an "optimal observer"-a channel or basis-that preserves $Φ$ better than any alternative. Finally, we propose a quantum Markov blanket theorem: the boundary of the optimal bipartition isolates subsystems most effectively. Our framework unites categorical enrichment, convex-geometric methods, and operational tools, forging a concrete bridge between integrated information theory and quantum information science.

quant-ph

Understanding and Evaluating Engineering Creativity:Development and Validation of the Engineering Creativity Assessment Tool (ECAT)

Creativity is essential in engineering education, enabling students to develop innovative and practical solutions. However, assessing creativity remains challenging due to a lack of reliable, domain-specific tools. Traditional assessments like the Torrance Tests of Creative Thinking (TTCT) may not fully capture the complexity of engineering creativity. This study introduces and validates the Engineering Creativity Assessment Tool (ECAT), designed specifically for engineering contexts. ECAT was tested with 199 undergraduate students who completed a hands-on design task. Five trained raters evaluated the products using the ECAT rubric. Exploratory and confirmatory factor analyses supported a four-factor structure: fluency, originality, cognitive flexibility, and creative strengths. Reliability was high, convergent and discriminant validity were examined using TTCT scores, revealing moderate correlations that support ECATs domain specificity. ECAT offers a reliable, valid framework for assessing creativity in engineering education and provides actionable feedback to educators. Future work should examine its broader applicability across disciplines and instructional settings.

stat.OT

Evaluating the capability of large language models to personalize science texts for diverse middle-school-age learners

Large language models (LLMs), including OpenAI's GPT-series, have made significant advancements in recent years. Known for their expertise across diverse subject areas and quick adaptability to user-provided prompts, LLMs hold unique potential as Personalized Learning (PL) tools. Despite this potential, their application in K-12 education remains largely unexplored. This paper presents one of the first randomized controlled trials (n = 23) to evaluate the effectiveness of GPT-4 in personalizing educational science texts for middle school students. In this study, GPT-4 was used to profile student learning preferences based on choices made during a training session. For the experimental group, GPT-4 was used to rewrite science texts to align with the student's predicted profile while, for students in the control group, texts were rewritten to contradict their learning preferences. The results of a Mann-Whitney U test showed that students significantly preferred (at the .10 level) the rewritten texts when they were aligned with their profile (p = .059). These findings suggest that GPT-4 can effectively interpret and tailor educational content to diverse learner preferences, marking a significant advancement in PL technology. The limitations of this study and ethical considerations for using artificial intelligence in education are also discussed.

cs.HC