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Electronics
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Techniques and Applications in Prompt Engineering and Generative AI
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Techniques and Applications in Prompt Engineering and Generative AI

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4686

Special Issue Editors

Dr. Marko Horvat

E-Mail Website
Guest Editor
Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
Interests: formal knowledge representation; automated reasoning; machine learning; information retrieval; Semantic Web
Special Issues, Collections and Topics in MDPI journals
Dr. Tomislav Jagušt

E-Mail Website
Guest Editor
Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
Interests: adaptive learning; educational technology; computer aided instruction; learning management systems; computer science education; educational robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Prompt engineering is a dynamic field that focuses on the development and optimization of generative AI chatbot prompts. In this regard, prompt engineering includes the development of algorithms and procedures for creating effective prompts, methods for evaluating the effectiveness of prompts, and the investigation of real-world case studies that demonstrate the benefits of prompt engineering applications. By advancing these areas, we aim to improve the functionality and usability of AI-driven programming tools.

Submissions may include research on advanced prompt design techniques, frameworks for evaluating prompt effectiveness, and detailed case studies of successful implementations in programming environments. In addition, we encourage interdisciplinary research that bridges the gap between rapid engineering and many other fields, including education, by providing insights into optimizing AI tools for both educators and learners.

The aim of this Special Issue is to identify the potential for prompt engineering in AI, not only in the educational tools but also in automated software development, information retrieval, natural language processing, improving collaboration in software development projects, as well as ethical considerations for prompt engineering and privacy concerns.

We encourage submissions that not only propose novel theories, but also implement and experimentally validate these concepts to ensure their practical applicability and reproducibility by other researchers.

The topics covered include, but are not restricted to, the following:

  • Advanced techniques for designing effective programming prompts;
  • Evaluation methodologies for prompt efficacy;
  • Case studies of prompt engineering in real-world programming scenarios;
  • The use of prompt engineering techniques in copilot-based coding assistants;
  • Educational applications of prompt engineering;
  • Frameworks for teaching prompt engineering in it and computer science courses;
  • Interdisciplinary approaches to prompt engineering and education;
  • Automated programming tools and how they depend on prompt engineering;
  • Ethical considerations for prompt engineering;
  • Prompt engineering to improve collaboration in software development projects;
  • Prompt engineering and domain-specific fine-tuning of large language models;
  • AI and privacy.

Dr. Marko Horvat
Dr. Tomislav Jagušt
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative AI
  • prompt engineering
  • copilot-based coding assistants
  • automated programming tools
  • evaluation methodologies

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Published Papers (3 papers)

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20 pages, 929 KiB  
Article
Use of Generative AI by Higher Education Students
by Ana Elisa Sousa and Paula Cardoso
Electronics 2025, 14(7), 1258; https://doi.org/10.3390/electronics14071258 - 22 Mar 2025
Viewed by 1770
Abstract
This research aims to explore the use, perceptions, and challenges associated with generative AI (GenAI) among higher education students. As GenAI technologies, such as language models, image generators, and code assistants, become increasingly prevalent in academic settings, it is essential to understand how [...] Read more.
This research aims to explore the use, perceptions, and challenges associated with generative AI (GenAI) among higher education students. As GenAI technologies, such as language models, image generators, and code assistants, become increasingly prevalent in academic settings, it is essential to understand how students engage with these tools and their impact on their learning process. The study investigates students’ awareness, adoption patterns, and perceptions of generative AI’s role in academic tasks, alongside the benefits they identify and the challenges they face, including ethical concerns, reliability, and accessibility. Through quantitative methods, the research provides a comprehensive analysis of student experiences with generative AI in higher education. The findings aim to inform educators, technologists, and institutions about the opportunities and barriers of integrating these technologies into educational practices and guide the development of strategies that support effective and responsible AI use in academia. Full article
(This article belongs to the Special Issue Techniques and Applications in Prompt Engineering and Generative AI)
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26 pages, 6237 KiB  
Article
Generative AI in Education: Perspectives Through an Academic Lens
by Iulian Întorsureanu, Simona-Vasilica Oprea, Adela Bâra and Dragoș Vespan
Electronics 2025, 14(5), 1053; https://doi.org/10.3390/electronics14051053 - 6 Mar 2025
Cited by 2 | Viewed by 2030
Abstract
In this paper, we investigated the role of generative AI in education in academic publications extracted from Web of Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency of keywords [...] Read more.
In this paper, we investigated the role of generative AI in education in academic publications extracted from Web of Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency of keywords over time; a keyword co-occurrence map; collaboration networks; and a Sankey diagram illustrating the relationship between AI-related terms, publication years and research areas; (2) Sentiment analysis using a custom list of words, VADER and TextBlob; (3) Topic modeling using Latent Dirichlet Allocation (LDA). Terms such as “artificial intelligence” and “generative artificial intelligence” were predominant, but they diverged and evolved over time. By 2024, AI applications had branched into specialized fields, including education and educational research, computer science, engineering, psychology, medical informatics, healthcare sciences, general medicine and surgery. The sentiment analysis reveals a growing optimism in academic publications regarding generative AI in education, with a steady increase in positive sentiment from 2023 to 2024, while maintaining a predominantly neutral tone. Five main topics were derived from AI applications in education, based on an analysis of the most relevant terms extracted by LDA: (1) Gen-AI’s impact in education and research; (2) ChatGPT as a tool for university students and teachers; (3) Large language models (LLMs) and prompting in computing education; (4) Applications of ChatGPT in patient education; (5) ChatGPT’s performance in medical examinations. The research identified several emerging topics: discipline-specific application of LLMs, multimodal gen-AI, personalized learning, AI as a peer or tutor and cross-cultural and multilingual tools aimed at developing culturally relevant educational content and supporting the teaching of lesser-known languages. Further, gamification with generative AI involves designing interactive storytelling and adaptive educational games to enhance engagement and hybrid human–AI classrooms explore co-teaching dynamics, teacher–student relationships and the impact on classroom authority. Full article
(This article belongs to the Special Issue Techniques and Applications in Prompt Engineering and Generative AI)
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17 pages, 1221 KiB  
Article
Advanced Prompt Engineering in Emergency Medicine and Anesthesia: Enhancing Simulation-Based e-Learning
by Charlotte Meynhardt, Patrick Meybohm, Peter Kranke and Carlos Ramon Hölzing
Electronics 2025, 14(5), 1028; https://doi.org/10.3390/electronics14051028 - 5 Mar 2025
Viewed by 816
Abstract
Medical education is rapidly evolving with the integration of artificial intelligence (AI), particularly through the application of generative AI to create dynamic learning environments. This paper examines the transformative role of prompt engineering in enhancing simulation-based learning in emergency medicine. By enabling the [...] Read more.
Medical education is rapidly evolving with the integration of artificial intelligence (AI), particularly through the application of generative AI to create dynamic learning environments. This paper examines the transformative role of prompt engineering in enhancing simulation-based learning in emergency medicine. By enabling the generation of realistic, context-specific clinical case scenarios, prompt engineering fosters critical thinking and decision-making skills among medical trainees. To guide systematic implementation, we introduce the PROMPT+ Framework, a structured methodology for designing, evaluating, and refining prompts in AI-driven simulations, while incorporating essential ethical considerations. Furthermore, we emphasize the importance of developing specialized AI models tailored to regional guidelines, standard operating procedures, and educational contexts to ensure relevance and alignment with current standards and practices. The framework aims to provide a structured approach for engaging with AI-generated medical content, allowing learners to reflect on clinical reasoning, critically assess AI-generated recommendations, and consider the potential role of AI tools in medical training workflows. Additionally, we acknowledge certain challenges associated with the use of AI in education, such as maintaining reliability and addressing potential biases in AI outputs. Our study explores how AI-driven simulations could contribute to scalability and adaptability in medical education, potentially offering structured methods for healthcare professionals to engage with generative AI in training contexts. Full article
(This article belongs to the Special Issue Techniques and Applications in Prompt Engineering and Generative AI)
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