The Impact of Prompt Engineering and a Generative AI-Driven Tool on Autonomous Learning: A Case Study

Education Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 199 - 199

Published: Feb. 7, 2025

This study evaluates “I Learn with Prompt Engineering”, a self-paced, self-regulated elective course designed to equip university students skills in prompt engineering effectively utilize large language models (LLMs), foster self-directed learning, and enhance academic English proficiency through generative AI applications. By integrating concepts tools, the supports autonomous learning addresses critical skill gaps market-ready capabilities. The also examines EnSmart, an AI-driven tool powered by GPT-4 integrated into Canvas LMS, which automates test content generation grading delivers real-time, human-like feedback. Performance evaluation, structured questionnaires, surveys were used evaluate course’s impact on prompting skills, proficiency, overall experiences. Results demonstrated significant improvements accessible patterns like “Persona” proving highly effective, while advanced such as “Flipped Interaction” posed challenges. Gains most notable among lower initial though engagement practice time varied. Students valued EnSmart’s intuitive integration accuracy but identified limitations question diversity adaptability. high final success rate that proper design (taking consideration Panadero’s four dimensions of learning) can facilitate successful learning. findings highlight AI’s potential task automation, emphasizing necessity human oversight for ethical effective implementation education.

Language: Английский

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Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 144 - 144

Published: March 4, 2025

The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning exam questions with course objectives, improving clarity difficulty, generating new items guided by learning goals. research spans four university courses—two theory-focused two application-focused—covering diverse cognitive levels according Bloom’s taxonomy. A balanced dataset ensures representation categories structures. Three LLM-based agents—VectorRAG, VectorGraphRAG, fine-tuned LLM—are developed evaluated against meta-evaluator, supervised human experts, assess alignment accuracy explanation quality. Robust analytical methods, including mixed-effects modeling, yield actionable insights integrating generative AI into processes. Beyond exam-specific applications, this methodology provides foundational approach the broader adoption post-secondary education, emphasizing fairness, contextual relevance, collaboration. findings offer comprehensive AI-generated content detailing effective integration strategies, addressing challenges such as bias limitations. Overall, work underscores potential while identifying pathways responsible implementation.

Language: Английский

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Bridging LMS and Generative AI: Dynamic Course Content Integration (DCCI) for Connecting LLMs to Course Content – The Ask ME Assistant

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 31, 2025

The integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) has the potential to enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a significant challenge. This study introduces Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves integrates course content curriculum from Canvas LMS into LLM-powered assistant, Ask ME. By employing prompt engineering structure retrieved within LLM’s context window, DCCI ensures accuracy, relevance, contextual alignment, mitigating hallucination. To evaluate DCCI’s effectiveness, ME’s usability, broader student perceptions AI education, mixed-methods approach was employed, incorporating user satisfaction ratings structured survey. Results pilot indicate high (4.614/5), students recognizing ability provide timely contextually relevant responses for both administrative course-related inquiries. Additionally, majority agreed that reduced platform-switching, improving engagement, comprehension. AI’s role reducing classroom hesitation fostering self-directed learning intellectual curiosity also highlighted. Despite these benefits positive perception tools, concerns emerged regarding over-reliance on AI, accuracy limitations, ethical issues such as plagiarism student-teacher interaction. These findings emphasize need strategic implementation, safeguards, pedagogical framework prioritizes human-AI collaboration over substitution. contributes AI-enhanced education by demonstrating how context-aware retrieval mechanisms like improve LLM reliability educational engagement while ensuring responsible integration.

Language: Английский

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Generative Artificial Intelligence as a Catalyst for Change in Higher Education Art Study Programs

Computers, Journal Year: 2025, Volume and Issue: 14(4), P. 154 - 154

Published: April 20, 2025

Generative Artificial Intelligence (AI) has emerged as a transformative tool in art education, offering innovative avenues for creativity and learning. However, concerns persist among educators regarding the potential misuse of text-to-image generators unethical shortcuts. This study explores how bachelor’s-level students perceive use generative AI artistic composition. Ten participated lecture on composition principles completed practical task using both traditional methods tools. Their interactions were observed, followed by administration questionnaire capturing their reflections. Qualitative analysis data revealed that recognize ideation conceptual development but find its limitations frustrating executing nuanced tasks. highlights current utility an inspirational mentor rather than precise tool, highlighting need structured training balanced integration with design methods. Future research should focus larger participant samples, assess evolving capabilities tools, explore to teach fundamental concepts effectively while addressing about academic integrity. Enhancing functionality these tools could bridge gaps between pedagogy education.

Language: Английский

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Chatbots as scaffolding tools: an active learning model to empower diverse learners

On the Horizon The International Journal of Learning Futures, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Purpose This study aims to investigate the scope of integrating educational bots using Active Learning Model (ALM) within Management System empower diverse learners in higher institutions (HEIs) focused on improving their academic performance. It also explores deliberations these chatbots ensure accessibility through personalised experience and better opportunities for underprivileged students community service by HEIs. Design/methodology/approach The systematic sampling method was adopted collect responses from 480 post-graduate departments HEIs situated Bangalore, Hyderabad, Trivandrum Chennai. JASP V.18 used perform Simple Percentage Analysis, Exploratory Factor Analysis Structural Equation Modelling validate hypothesis. ALM dimensions resulted learning, intelligent tutoring, language learning analytics accessibility. Findings Personalised tutoring earning are key indicators dimensions. Intelligent is highest predictor chatbots. significantly impacts among Originality/value covers complexities chatbots, theoretical foundations active a methodology offers an interdisciplinary approach that provides insights recommendations will guide future practices policy creation promote robust research, ultimately advancing inclusive education digital era.

Language: Английский

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The Emergence of Generative AI in Higher Education

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 73 - 96

Published: April 25, 2025

The emergence of Generative AI (GAI) in education brings both benefits and challenges. As GAI tools become more common schools, concerns about ethics, academic honesty, how well teachers students adapt to are a major concern. This chapter explored the challenges using as experienced by rural areas, where access technology digital skills may affect use. Following Husserlian phenomenological research design, participants were interviewed, transcripts examined thematic analysis. findings show that struggle balance with traditional teaching, while face literacy integrity. Despite these issues, see potential improving learning. emphasizes need for clear ethical guidelines, training, school support ensure used responsibly. These importance further on AI's role education.

Language: Английский

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