EPIAG-ES Scale ------------------- General Information: ------------------------- Dataset Title: DATASET: VALIDATION OF EPIAG-ES SCALE Authors: María Inmaculada Jiménez Perona, Miguel Ángel Fernández Jiménez, and Juan José Leiva Olivencia Contact Information: María Inmaculada Jiménez Perona – mijperona@uma.es Methodological Information: ---------------------------------- 1.1. Procedure The procedure for designing the questionnaire started with an exhaustive review of the literature on the subject, from which the objectives, dimensions, and variables of the study were established. A provisional questionnaire was drafted and evaluated by a group of 10 expert professionals from the Faculty of Education Sciences at the University of Málaga. The questionnaire was designed with an organized and clear approach in mind. The following aspects were taken into account: 1. An introduction was included, containing the objective of the questionnaire. 2. The questionnaire was organized by dimensions to facilitate comprehension and response fluency. 3. It was divided into sections and included numbered questions for clear identification and easy tracking. After making modifications based on the experts' suggestions, the final questionnaire was created and applied to a sample of the target population. Participants responded to the questionnaire online via the internet so that it was available 24 hours a day. The students’ responses were recorded in a spreadsheet to facilitate subsequent data analysis. 1.2. Objective To evaluate the impact, pedagogical implications, and attitudes derived from the use of generative artificial intelligence (GAI) applications in teaching-learning processes in higher education. 1.3. Design Quantitative research methodology with a descriptive approach. A survey-based design was used, which allowed the collection of information on the variability of the different dimensions analyzed in the study. 1.4. Sample A non-probability sampling method of the casual or accidental type was used to select the participants. The selected sample consisted of a total of 471 students currently enrolled in university studies. The psychometric characteristics of the sample were obtained through an analysis of the distributions of the socio-demographic variables age, gender, academic program, and year of study. The sample consisted of students aged between 18 and 57 years. The mean age was 21.26 years (SD = 5.56), the mode was 20, and the median was 20. Regarding gender, the sample was composed of 77% women, 22% men, and 1% other responses (prefer not to say, non-binary, etc.). 41% of the subjects were in the second year, followed by 31% in the first year, 18% in the third year, and 5% in the fourth year and in Master's programs, respectively. 1.5. Instrument Structure The questionnaire, divided into 3 sections, consists of a total of 49 items, plus 5 referring to assigned or attributive variables: • The first section consists of 5 items gathering additional information regarding the student’s age, gender, university, academic program, and current year of study. • The second section is the main body of the questionnaire and includes 42 items according to an ordinal scale related to the first five dimensions of the study, with categories established on a scale from 1 to 5, based on the degree of agreement the respondent has with each item (from 1 = Strongly disagree to 5 = Strongly agree). • The third section includes 7 items related to the sixth study dimension “Types of Generative Artificial Intelligence Applications.” In these items, students can select from different types of GAI applications according to their functions. 1.6. Dimensions and variables For the elaboration of the questionnaire, 6 dimensions were taken into account, with a total of 47 variables. Based on these, the different items of the instrument were determined. • Dimension 1. Perception of knowledge and attitudes toward GAI: How generative artificial intelligence (GAI) is understood and responded to in the educational experience and preparation for professional future. Variables: Familiarity with the concept of GAI. Knowledge about the functioning of GAI. Competencies for using GAI tools in academic tasks. Improvement of learning through GAI. Employment impact of knowing how to use GAI. Importance of GAI for professional development. Impact of GAI on learning. Influence of GAI in university education. Reliability of GAI-generated information. Interest in GAI. Pedagogical implications of GAI. Participation in GAI training. • Dimension 2. Benefits and opportunities of GAI: How the positive aspects and opportunities that generative artificial intelligence (GAI) may offer in the educational experience and preparation for professional future are perceived. Variables: Creative potential of GAI. Academic efficiency with GAI. Agility and efficiency in academic tasks with GAI. Effective personalization of learning with GAI. Efficient design of educational materials with GAI. Access to relevant information with GAI. • Dimension 3. Challenges and difficulties of GAI: How the obstacles and difficulties associated with the use of generative artificial intelligence (GAI) in the educational experience and professional preparation are perceived. Variables: Technological dependence on GAI. Impact on learning autonomy with GAI. Influence of GAI on formative interaction. Risk of superficial learning with GAI. Interference of GAI in critical thinking and creativity. Resistance to educational use of GAI. Teacher training in GAI. • Dimension 4. Use of GAI: How the educational use of generative artificial intelligence (GAI) in the learning process is perceived and experienced. Variables: Use of GAI tools. Personalization of information with GAI. Immediate feedback from GAI. Time optimization with GAI. Use of GAI in multimedia content production. Use of GAI in information synthesis. Use of GAI in text paraphrasing. Improvement of outputs with GAI. • Dimension 5. Ethical aspects related to GAI: How the ethical implications associated with the use of generative artificial intelligence (GAI) in the educational context of higher education and data privacy are perceived and understood. Variables: Concern about the security of GAI systems. Concern about biased information in GAI. Concern about perpetuating stereotypes in GAI. Transparency in GAI-based tools. Regulation of GAI in university education. Accountability and supervision in the use of GAI. Concern about the negative impact of GAI. • Dimension 6. Types of GAI applications: How different types of generative artificial intelligence (GAI) applications are perceived and used in the context of higher education, covering a wide range of possibilities including text, image, sound, video, and other formats. Variables: GAI applications related to text. GAI applications related to images. GAI applications related to audio and sound. GAI applications related to video. GAI applications related to data analysis. GAI applications related to translation. Other GAI applications used. -------------------- More information: María Inmaculada Jiménez Perona. mijperona@uma.es