Sep 2026· Currents in Pharmacy Teaching and Learning· Vol 18 12, pp.
102809
· 0 citations· 6 references
Medicine
Abstract
Background
Generative artificial intelligence (AI) is increasingly integrated into pharmacy education, with calls for intentional, ethical instruction to support AI literacy and professional competencies.
EDUCATIONAL ACTIVITY
We piloted a semester-long sequence of seven scaffolded assignments supporting an argumentative synthesis paper in a required first-semester PharmD course, permitting allowed and disclosed AI use across multiple stages of the research and writing process.
EVALUATION
Findings
Quantitative data demonstrated consistently high student performance across assignments, with average scores near maximum values, and widespread but variable AI use across tasks. Course evaluations indicated favorable overall perceptions (n = 112/120; 93.3% response rate; 4.04/5). Among the limited comments that referenced AI, students described both perceived benefits and concerns. These comments, together with Collaborative Learning Community (CLC) insights, suggested that AI-supported activities were better received when optional, purposefully integrated, and aligned with learning outcomes.
ANALYSIS OF EDUCATIONAL ACTIVITY
Findings suggest that student responses to AI integration are driven primarily by instructional design rather than the technology itself. Transparent purpose, intentional sequencing of AI use, and preservation of student autonomy may enhance engagement and perceived learning value.
Conclusion
A TILT-informed, student-partnered approach offers a practical framework for integrating AI into pharmacy education while supporting meaningful learning and AI literacy development. Future evaluation will examine student experiences via focus groups and interviews and survey data will examine changes in AI literacy.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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