OwnYourAI Enterprise Analysis
Development and validation of generative artificial intelligence attitude scale for students
This research developed and validated a Generative AI Attitude Scale for university students, addressing a gap in measuring student perceptions of AI tools like ChatGPT in education. Through a three-stage process involving expert evaluation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) with data from 664 students, a reliable and valid 13-item, two-factor scale was established. The scale measures both positive and negative attitudes, demonstrating strong psychometric properties (Cronbach's alpha of 0.84, test-retest reliability of 0.90) and discriminative power. The findings provide a robust instrument for future research and inform implementation strategies to integrate generative AI effectively in educational settings.
Executive Impact Snapshot
Key metrics from "Development and validation of generative artificial intelligence attitude scale for students" highlight the immediate relevance and potential for enterprise AI applications.
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Students Surveyed
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Final Scale Items
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Key Attitude Factors
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% Variance Explained (EFA)
Deep Analysis & Enterprise Applications
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Introduction
Research Background
Methodology
Results
Discussion and Conclusion
Introduction
In today's digital era, the symbiotic relationship between humans and computers has evolved into an inseparable facet of modern existence. Digital technology, in its myriad forms, has revolutionized how we communicate, work, and interact with the world around us. Human-computer interaction (HCI) stands as the cornerstone of this transformation, permeating nearly every aspect of daily life. HCI has become a pivotal aspect of education, reshaping the way students and educators engage with learning material, communicate, and collaborate (Zawacki-Richter et al., 2019). The advent of generative artificial intelligence (AI) tools, such as ChatGPT, heralds a further evolution in the educational landscape, promising to enhance personalized learning and foster student creativity (Marengo et al., 2023).
Research Background
Generative AI technology can potentially enhance significantly personalised educational content and materials, including text, pictures, and videos. By utilising generative AI, educational systems can provide customised learning resources that cater specifically to the unique characteristics of individual students, such as their learning needs, preferences, and pace (Hsu and Ching, 2023; Pataranutaporn et al., 2021). This advanced technology allows students to create learning materials, leading to a more engaging and enjoyable learning experience. As a result, personalised instruction facilitated by generative AI technology combined with student-generated content creation abilities enhances overall comprehension levels among learners and higher-order thinking skills while increasing satisfaction with the course (Elfeky, 2019; Zhang et al., 2020). Consequently, students feel empowered through self-directed knowledge generation when supported by an environment integrating generative AI technology. Yet, it's crucial to confront apprehensions regarding usability, accessibility, and their potential effects on how students think and behave (Baytak, 2023).
Methodology
A three-stage process was employed to develop and validate the Generative AI Attitude Scale. Data were collected from 664 students from various faculties during the 2022-2023 academic year. Expert evaluations were conducted to establish face and content validity. An exploratory factor analysis (EFA) was performed on a subset of 400 participants, revealing a two-factor, 14-item structure that explained 78.440% of the variance. A subsequent confirmatory factor analysis (CFA) was conducted on a separate sample of 264 students to validate this structure, resulting in the removal of one item and a final 13-item scale. The 13-item scale demonstrated strong reliability, evidenced by a Cronbach's alpha of 0.84 and a test-retest reliability of 0.90.
Results
The 13-item scale demonstrated strong reliability, evidenced by a Cronbach's alpha of 0.84 and a test-retest reliability of 0.90. Discriminative power was confirmed through corrected item-total correlations between lower and upper percentile groups. These findings indicate that the scale effectively differentiates student attitudes toward generative AI tools in educational contexts. The two-factor structure revealed through our analyses warrants deeper theoretical interpretation. Each factor encompasses distinct but interrelated dimensions of students' attitudes toward generative AI in educational contexts. The positive attitude factor (Items 1-8) focuses on perceived educational utility, cognitive development support, engagement motivation, and technology appreciation. The negative attitude factor (Items 10-13) covers future impact concerns, learning process concerns, and reliability/trust issues. All items meet the criterion for satisfactory discriminative power (item-total correlations > 0.30).
Discussion and Conclusion
The newly developed Generative AI Attitude Scale offers a valid and reliable instrument for measuring university students' perspectives on integrating generative AI tools, such as ChatGPT, into educational environments. These results highlight the potential for more targeted research and informed implementation strategies to enhance learning outcomes through generative AI. The study acknowledges several limitations, including a sample primarily of university students using ChatGPT, and suggests future research could broaden participant diversity and explore long-term effects. This validated scale provides a foundational tool for understanding and shaping attitudes toward AI in education, ultimately enhancing human-computer interaction and educational experiences.
Enterprise Process Flow
Item Generation (21 items)
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Student Review (18 items)
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Expert Opinion (14 items)
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Pilot Testing (14 items)
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Scale Administration & Validation (13 items final)
0.84
Cronbach's Alpha (Internal Consistency)
| Attitude Type | Description | Key Sub-dimensions |
|---|---|---|
| Positive Attitude | Focuses on immediate educational benefits and learning enhancement. |
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| Negative Attitude | Addresses broader implications for personal development and societal impact. |
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0.90
Test-Retest Reliability (Stability)
ROI Calculator for AI Integration
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Your AI Implementation Roadmap
A strategic approach to integrating generative AI, informed by "Development and validation of generative artificial intelligence attitude scale for students", ensures successful adoption and maximized benefits.
Phase 1: Readiness Assessment
Utilize the Generative AI Attitude Scale to measure student and educator readiness. Identify specific concerns and enthusiasm levels to tailor initial AI integration strategies.
Duration: 1-2 Weeks
Phase 2: Pilot Program & Feedback
Implement pilot programs with targeted generative AI tools. Gather qualitative and quantitative feedback, focusing on perceived utility and addressing early challenges, leveraging the scale for continuous assessment.
Duration: 4-6 Weeks
Phase 3: Curricular Integration & Training
Develop and integrate AI-supported learning modules into the curriculum. Provide comprehensive training for both students and faculty on ethical AI use, data privacy, and maximizing learning outcomes.
Duration: 8-12 Weeks
Phase 4: Ongoing Monitoring & Refinement
Continuously monitor the impact of generative AI on learning outcomes and student attitudes. Use iterative feedback loops and scale re-administration to refine implementation strategies and adapt to evolving AI capabilities.
Duration: Ongoing
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"Development and validation of generative artificial intelligence attitude scale for students" provides critical insights into student perceptions of AI in education. Apply these learnings to your enterprise for a smooth, effective AI adoption.
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