prompt-engineering
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- PaperComputers & Education10 Jul 2026
Educational prompt engineering self-efficacy scale (Ed-PESS): Development and psychometric validation
Fatih Karataş, Recep GÜR, Barış Eriçok, Fatma BAŞARIR et al.
A new scale called the Educational Prompt Engineering Self-Efficacy Scale (Ed-PESS) was developed and psychometrically validated to measure educators' confidence in using prompt engineering for educational purposes.
- PaperarXiv — AI in Education (cs.CY)29 Jun 2026
Teaching Prompt-Based Programming with LLMs: A 45-Minute Lesson with Guided Practice for End-User Programmers
Keith Tran, Samiha Marwan, Thomas Price
A 45-minute lesson on prompt-based programming with LLMs was evaluated against a traditional lab activity. Students receiving the prompt-focused instruction showed greater gains in prompting self-efficacy and modest improvements in programming performance. The results suggest that brief interventions can help but more practice is likely needed.
Original abstract
Prompt-based programming, a new modality enabled by large language models (LLMs), allows users to express computational goals through natural language rather than traditional code. While this approach lowers barriers to entry, especially for non-CS learners, it does not eliminate the need for foundational CS skills. Learners often struggle to communicate their intent clearly to LLMs, resulting in vague or underspecified prompts. Prior work has documented the need for explicit prompting for both CS and non-CS learners. However, it remains less clear how such instruction can fit into busy classrooms or how much time is needed to produce meaningful gains. In this paper, we evaluated a 45-minute prompt-based programming intervention, consisting of a lesson with guided practice, against a business-as-usual CS lab activity (code tracing) of equal length, representing a class without prompt-focused instruction. We conducted a randomized controlled study with 55 engineering students. We found that students in the experimental condition improved more on average (though not significantly more) from pre- to post-test than the control group (+10.8 vs +1.1 percentage points) and showed significantly greater average gains in prompting self-efficacy (+35.4 vs +21.9 percentage points). Our results suggest it is likely that a brief intervention can improve learners' ability to specify computational goals to LLMs. However, the effect was modest, suggesting that prompting skills may require more time and practice to develop. We provide a lightweight lesson that requires no prior CS background and can be readily dropped into existing courses.
- PaperComputers and Education: Artificial Intelligence21 Jun 2026
Students’ multimodal prompting practices as epistemic work in AI literacy development
Sylvana Sofkova Hashemi
Investigates prompting strategies of 28 postgraduate students using a generative AI tool in collaborative multimodal tasks, finding strategies ranging from basic input-output to strategic, iterative, and dialogic practices. Prompting emerges as an epistemic practice for AI literacy, fostering critical interpretation and awareness of system limitations, while ethical dimensions remain underdeveloped. The study highlights the value of iterative, reflective, and multimodal learning designs for fostering critical and strategic engagement with AI.
Original abstract
: As generative artificial intelligence (GenAI) rapidly transforms higher education, critical questions arise about how students engage with these open-ended tools and the implications for learning. This study provides empirical insight into this research gap investigating (1) the prompting strategies students develop when interacting with a university-provided GenAI tool and (2) how engagement in prompt engineering activities shapes their understanding of GenAI and AI literacy. Data were collected in an exploratory workshop with 28 postgraduate students engaged in collaborative multimodal prompting tasks, including the creation of short stories or poems and corresponding images. Students’ self-documented prompting histories and reflections were analysed qualitatively using reflexive thematic analysis, guided by frameworks for prompting methods and AI literacy. The findings show that students’ prompting strategies vary along a continuum from basic input-output use to strategic, iterative, and dialogic practices. Prompting emerges as a central epistemic practice through which students critically interpret, refine, and negotiate AI-generated outputs. Multimodal engagement exposes challenges in translating abstract meaning into machine-readable prompts, fostering awareness of system limitations, bias, and the need to actively construct coherence across modalities. While students demonstrate developing competence in evaluation and creation, ethical dimensions of AI literacy remain underdeveloped. The findings provide empirical insight into how AI literacy develops through hands-on engagement with GenAI, positioning prompting as an epistemic practice through which students learn to interpret, negotiate, and guide AI-generated outputs, while highlighting the value of iterative, reflective, and multimodal learning designs that foster critical, strategic, and responsible engagement with AI.