Transforming GenAI Policy to Prompting Instruction: An RCT of Scalable Prompting Interventions in a CS1 Course

• URL: http://arxiv.org/abs/2602.16033v1
• Date: Tue, 17 Feb 2026 21:40:12 GMT
• Title: Transforming GenAI Policy to Prompting Instruction: An RCT of Scalable Prompting Interventions in a CS1 Course
• Authors: Ruiwei Xiao, Runlong Ye, Xinying Hou, Jessica Wen, Harsh Kumar, Michael Liut, John Stamper,
• Abstract summary: We conducted a semester-long RCT with four ICAP framework-based instructional conditions varying in engagement intensity with a pre-test, immediate and delayed post-test and surveys.<n>We found that all conditions significantly improved prompting skills, with gains increasing progressively from Condition 1 to Condition 4.<n>For students with similar pre-test scores, higher learning gain in immediate post-test predict higher final exam score, though no direct between-group differences emerged.
• Score: 8.222598094097867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite universal GenAI adoption, students cannot distinguish task performance from actual learning and lack skills to leverage AI for learning, leading to worse exam performance when AI use remains unreflective. Yet few interventions teaching students to prompt AI as a tutor rather than solution provider have been validated at scale through randomized controlled trials (RCTs). To bridge this gap, we conducted a semester-long RCT (N=979) with four ICAP framework-based instructional conditions varying in engagement intensity with a pre-test, immediate and delayed post-test and surveys. Mixed methods analysis results showed: (1) All conditions significantly improved prompting skills, with gains increasing progressively from Condition 1 to Condition 4, validating ICAP's cognitive engagement hierarchy; (2) for students with similar pre-test scores, higher learning gain in immediate post-test predict higher final exam score, though no direct between-group differences emerged; (3) Our interventions are suitable and scalable solutions for diverse educational contexts, resources and learners. Together, this study makes empirical and theoretical contributions: (1) theoretically, we provided one of the first large-scale RCTs examining how cognitive engagement shapes learning in prompting literacy and clarifying the relationship between learning-oriented prompting skills and broader academic performance; (2) empirically, we offered timely design guidance for transforming GenAI classroom policies into scalable, actionable prompting literacy instruction to advance learning in the era of Generative AI.

Related papers

• Test-Time Learning of Causal Structure from Interventional Data [50.06913286558919]
  We propose TICL (Test-time Interventional Causal Learning), a novel method that synergizes Test-Time Training with Joint Causal Inference.<n>Specifically, we design a self-augmentation strategy to generate instance-specific training data at test time, effectively avoiding distribution shifts.<n>By integrating joint causal inference, we developed a PC-inspired two-phase supervised learning scheme, which effectively leverages self-augmented training data while ensuring theoretical identifiability.
  arXiv  Detail & Related papers  (2026-02-22T11:23:05Z)
• Designing AI-Resilient Assessments Using Interconnected Problems: A Theoretically Grounded and Empirically Validated Framework [0.0]
  The rapid adoption of generative AI has undermined traditional modular assessments in computing education.<n>This paper presents a theoretically grounded framework for designing AI-resilient assessments.
  arXiv  Detail & Related papers  (2025-12-11T15:53:19Z)
• UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models [59.693733170193944]
  Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings.<n>Recent reinforcement learning approaches address this limitation but face two critical challenges.<n>We propose the Unidirectional Cognitive Optimization (UCO) method to address these challenges.
  arXiv  Detail & Related papers  (2025-11-12T01:27:02Z)
• Assessment Twins: A Protocol for AI-Vulnerable Summative Assessment [0.0]
  We introduce assessment twins as an accessible approach for redesigning assessment tasks to enhance validity.<n>We use Messick's unified validity framework to systematically map the ways in which GenAI threaten content, structural, consequential, generalisability, and external validity.<n>We argue that the twin approach helps mitigate validity threats by triangulating evidence across complementary formats.
  arXiv  Detail & Related papers  (2025-10-03T12:05:34Z)
• Learning to Use AI for Learning: How Can We Effectively Teach and Measure Prompting Literacy for K-12 Students? [1.413488665073795]
  There is a growing need to equip the next generation with the ability to apply, interact with, evaluate, and collaborate with AI systems responsibly.<n>To address this need, we designed an Large-Language Model (LLM)-based module to teach prompting literacy.<n>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.
  arXiv  Detail & Related papers  (2025-08-19T15:54:51Z)
• AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories [8.500617875591633]
  This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI)<n>The framework emphasizes transparency, self-regulated learning, and pedagogical oversight.
  arXiv  Detail & Related papers  (2025-08-01T15:44:19Z)
• AI instructional agent improves student's perceived learner control and learning outcome: empirical evidence from a randomized controlled trial [29.45751702212421]
  This study examines the impact of an AI instructional agent on students' perceived learner control and academic performance in a medium demanding course with lecturing as the main teaching strategy.<n>Students in the AI instructional agent group reported significantly higher levels of perceived learner control compared to the other groups.
  arXiv  Detail & Related papers  (2025-05-28T16:13:27Z)
• Online inductive learning from answer sets for efficient reinforcement learning exploration [52.03682298194168]
  We exploit inductive learning of answer set programs to learn a set of logical rules representing an explainable approximation of the agent policy.<n>We then perform answer set reasoning on the learned rules to guide the exploration of the learning agent at the next batch.<n>Our methodology produces a significant boost in the discounted return achieved by the agent, even in the first batches of training.
  arXiv  Detail & Related papers  (2025-01-13T16:13:22Z)
• Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning [99.05401042153214]
  In-context learning (ICL) is potentially attributed to two major abilities: task recognition (TR) and task learning (TL) We take the first step by examining the pre-training dynamics of the emergence of ICL. We propose a simple yet effective method to better integrate these two abilities for ICL at inference time.
  arXiv  Detail & Related papers  (2024-06-20T06:37:47Z)
• Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality [55.88910947643436]
  Self-supervised pre-training is essential for handling vast quantities of unlabeled data in practice. HiDe-Prompt is an innovative approach that explicitly optimize the hierarchical components with an ensemble of task-specific prompts and statistics. Our experiments demonstrate the superior performance of HiDe-Prompt and its robustness to pre-training paradigms in continual learning.
  arXiv  Detail & Related papers  (2023-10-11T06:51:46Z)
• Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble [56.705249154629264]
  Self-training teacher-student frameworks are proposed to improve the robustness of NER models. In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks. Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
  arXiv  Detail & Related papers  (2022-12-13T12:14:09Z)
• Autonomous Reinforcement Learning: Formalism and Benchmarking [106.25788536376007]
  Real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world. Common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts. This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms.
  arXiv  Detail & Related papers  (2021-12-17T16:28:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.