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Browsing Masters by Subject "ChatGPT"
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Item Open Access A Comparative Study of AI-assisted Assessment and Teacher-Assessment in an EFL Writing Course(SDU University, 2025) Fazilova A.With the rapid development of artificial intelligence in education, automated writing evaluation (AWE) tools such as ChatGPT are becoming increasingly popular for providing feedback on students' written works. This study explores the perceptions of first-year EFL students about AI-assisted assessment compared to teacher assessment in an EFL writing course at a private Kazakhstani university. Over the period of one semester, 33 participants have written 4 essays and received both the teacher and ChatGPT feedback on each essay. Their perceptions have been compared and analyzed with a quantitative research design with elements of qualitative analysis. A questionnaire with Likert-scale closed-ended and open-ended questions was used. The results revealed that while students recognize the importance of AI-assisted assessment for surface-level corrections (grammar, vocabulary, structure), the majority of students prefer teacher feedback for its clarity, personalized support, and depth. Additionally, most students viewed the ideal approach as a combination of two types of feedback: AI for quick technical feedback and teachers for more complex aspects like structure, argumentation, and tone. The study concludes that although AWE tools have potential as supplementary support in EFL writing instruction, they cannot replace the human connection and pedagogical insight offered by teachers. Implications for integrating AI tools into classroom practice and teacher training are also discussed, along with recommendations for future research in this evolving field.Item Open Access Integration of Technology in Chemistry Education(SDU University, 2025) Daniyarkyzy L.This section details the research methods used to implement technology into chemical education research. The study adopts a quantitative methodology, specifically a survey method based on a systematic study of the impact and perceptions of the use of technology by students. The focus for this project was to obtain objective data in order to recognize trends, correlations and total impact of the technology use. The study included students from multiple levels of learning such as high school students, undergraduates completing a chemistry course, and possibly graduate students which allows the study to have more holistic picture of the chemistry learning experience. They were a broad array of students so we could consider an extensive range of experiences with respect to one's views on technology in educational settings. Data collection was primarily through a thorough and systematic survey tool specifically developed to derive both closed (for instance, on the Likert scale) and open responses. The survey tool has specific emphasis on the examination of students experiences, attitudes to learning, perceived benefits, drawbacks and frequencies of the use of diverse technological tools in chemistry learning environments. Key areas of research included their interaction with simulators, virtual labs, educational software, and online resources. The analytical methods were strictly based on a statistical analysis of the collected survey results. This included both descriptive statistics (e.g., frequencies, averages, standard deviations) to summarize the demographic data of participants and their overall responses, and logicalstatistics (e.g., t-tests, ANOVA, correlation analysis) to identify statistically significant patterns, correlations, and meaningful outcomes. The aim was to draw informed conclusions about the relationship between the integration of technology and the results of students, their perception and involvement in the process of studying chemistry.Item Open Access THE TASK DEVELOPMENT OF PHYSICS SUBJECT TEACHERS USING CHATGPT.(SDU University, 2025) Amirkhan D.This study explores the effectiveness of ChatGPT in solving of physics problems of varying difficulty levels across different topics as a generative AI language model. We mainly do aim to evaluate just how the model performs as well as integrate the model into physics education. The succeeding queries provide guidance to the research: With what accuracy does ChatGPT solve the physics problems at a variety of difficulty levels? Do certain specific physics topics vary in performance regarding amount? ChatGPT performs to a greatly better degree on low-difficulty problems, the study hypothesizes, and shows a stronger conceptual understanding rather than a numerical accuracy. Theoretical frameworks grounding the research are Technological Pedagogical Content Knowledge (TPACK), as well as the SAMR model (Substitution, Augmentation, Modification, Redefinition) used to assess the pedagogical value of AI integration in teaching. ChatGPT was in fact tested on a total of 105 physics problems, drawn from a total of seven topical areas, and a design which was quantitative and descriptivecomparative was adopted. Each response was scored through use of a structured rubric for conceptual understanding. The final accuracy was also able to be scored. Data were analyzed by descriptive statistics and t-tests. ChatGPT performs well enough on formula-based, low-difficulty problems, especially inside mechanics, as findings indicate, yet struggles upon optics and electromagnetism, abstract, complex topics. With consistency, conceptual understanding in scores happened to be higher than the accuracy of answers. The study offers several practical recommendations for integrating ChatGPT into STEM instruction. The study does also contribute in a theoretical way by applying both TPACK and also SAMR to AIassisted learning. It concludes instruction that is teacher-led should be supplemented, and not replaced, while ChatGPT can improve learning through its guided use.