ARPG+: Teaching Students to Ask Effective Questions for Educational LLM Use

Why It Matters

By turning prompt engineering into a teachable skill, ARPG+ reduces dependence on trial‑and‑error and accelerates AI literacy, a critical competency for the future workforce. Its adaptive model promises broader adoption of LLMs in education without overwhelming students.

Key Takeaways

• ARPG+ boosts prompt quality by 143% versus unguided practice
• Learners achieve independent prompting in 91% of final interactions
• System adapts support using cognitive load signals and uncertainty metrics
• Dual architecture delivers fast feedback, reserving deep analysis for critical moments

Pulse Analysis

The rapid diffusion of large language models (LLMs) across K‑12 and higher‑education settings has outpaced students' ability to interact with them effectively. While LLMs can generate essays, solve problems, or simulate experiments, the quality of output hinges on the precision of the user's prompt. Traditional teaching methods rely on static templates or post‑hoc feedback, which fail to address the dynamic nature of learning and often leave students stuck in a cycle of guesswork. This gap hampers the development of critical thinking and limits the educational value of AI tools.

ARPG+ tackles this challenge by embedding cognitive load theory and the zone of proximal development into a real‑time coaching engine. The system continuously monitors behavioral cues—such as hesitation time and error patterns—to quantify learner uncertainty and detect overload. It then delivers calibrated interventions across six dimensions of prompt quality, gradually fading assistance as competence grows. A lightweight‑deep dual architecture ensures instantaneous responses for routine queries while allocating richer analytical resources to high‑stakes interactions, making the platform both responsive and scalable across subjects.

The implications extend beyond classroom walls. By automating the scaffolding of prompt‑engineering skills, ARPG+ equips a generation of students with transferable AI literacy, reducing reliance on ad‑hoc trial‑and‑error. Educational institutions can integrate the tool without extensive retraining, and its domain‑agnostic design suggests applicability in corporate training, research labs, and public‑sector upskilling programs. As AI becomes a foundational layer of the knowledge economy, solutions that democratize effective LLM use will be pivotal in shaping a competitive, inclusive workforce.