ArticleScientific reports2026
Development and validation of the student readiness and anxiety scale for AI-assisted biology teaching (SRAS-AIBT).
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Development and validation of the digital competence framework and scale for pre-service physical education teachers.Scientific reports · 2026Article
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Authors and funding
2 authors.
Funding
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Abstract
As the use of artificial intelligence in education increases, determining student readiness and anxiety has become a necessity; however, the lack of a measurement tool specific to biology education in the literature formed the basis of this study. This study aims to address this gap by developing a scale that can measure student readiness and anxiety about the use of artificial intelligence in biology teaching. This research employed an exploratory sequential mixed-methods design. First, new items were developed and an item pool created by gathering student opinions based on relevant concepts and literature reviews. Then, the prepared items were submitted to expert opinions, and after expert evaluations, validity and reliability tests of the scale were conducted using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). This study involved 322 students in the EFA phase and 300 students in the CFA phase, aged 18-22, studying biology in a province in eastern Turkey. The developed scale consisted of two factors and 15 items. The scale was developed as a 5-point Likert-type instrument ranging from 'Strongly Disagree' (1) to 'Strongly Agree' (5). According to the EFA results, the cumulative variance ratio was determined to be 73.084%. The Cronbach's alpha reliability coefficient was calculated as 0.95. The CFA results revealed that the two-factor model had a sufficient fit, and the values χ²/df = 3.7, RMSEA = 0.08, CFI = 0.94, and NNFI (TLI) = 0.93 support the structural validity of the model. As a reliable and valid instrument, the SRAS-AIBT serves as a practical tool for educators to design AI-assisted biology lessons tailored to student needs, thereby contributing directly to the improvement of instructional methods in this rapidly evolving field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.