Date
Publisher
arXiv
As Artificial Intelligence (AI) becomes increasingly integrated into daily life, there is a growing need to equip the next generation with the ability to apply, interact with, evaluate, and collaborate with AI systems responsibly. Prior research highlights the urgent demand from K-12 educators to teach students the ethical and effective use of AI for learning. To address this need, we designed an Large-Language Model (LLM)-based module to teach prompting literacy. This includes scenario-based deliberate practice activities with direct interaction with intelligent LLM agents, aiming to foster secondary school students' responsible engagement with AI chatbots. We conducted two iterations of classroom deployment in 11 authentic secondary education classrooms, and evaluated 1) AI-based auto-grader's capability; 2) students' prompting performance and confidence changes towards using AI for learning; and 3) the quality of learning and assessment materials. Results indicated that the AI-based auto-grader could grade student-written prompts with satisfactory quality. In addition, the instructional materials supported students in improving their prompting skills through practice and led to positive shifts in their perceptions of using AI for learning. Furthermore, data from Study 1 informed assessment revisions in Study 2. Analyses of item difficulty and discrimination in Study 2 showed that True/False and open-ended questions could measure prompting literacy more effectively than multiple-choice questions for our target learners. These promising outcomes highlight the potential for broader deployment and highlight the need for broader studies to assess learning effectiveness and assessment design.
Study at a glance
- Question: Can an interactive module with automated feedback improve secondary students' prompting skills and confidence in using AI for learning?
- Setting and data: 11 secondary school classrooms in East Asia, June 2024, including 111 students with valid data in Study 1.
- Method: Mixed methods design including automated grading evaluation, pre- and post-tests, student surveys, and psychometric item analyses across two classroom studies.
Why it matters
Few instructional interventions teach prompting literacy to K-12 students, despite an urgent demand from educators to equip students to use artificial intelligence chatbots responsibly and effectively for learning.
Key findings
- The AI auto-grader achieved 0.92 overall accuracy in evaluating students' written prompts compared to human grading.
- Students significantly increased their self-reported confidence in using AI to help them learn by 10.4% following the interactive practice.
- Practicing with the tool significantly increased the proportion of students who correctly included background and context in their prompts.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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