Jul 2026· Language Teaching Research· 0 citations· 54 references
TL;DR
The findings revealed that in evaluations conducted without a rubric, teachers were limited in their ability to distinguish individual differences and demonstrated low scoring consistency, while in evaluations conducted using a rubric, scoring consistency increased in both groups, although, as in the first evaluation, artificial intelligence tools demonstrated a higher level of consistency.
Abstract
This study examined the use of artificial intelligence tools, which have garnered significant attention in recent years, in the assessment and evaluation processes of language education. For this purpose, student essays were scored by Turkish middle school teachers and artificial intelligence tools both with and without the use of a rubric, and the findings were evaluated based on generalizability theory. Additionally, the research findings were shared with participants to gather qualitative data, which were analysed using the inductive thematic analysis method to support the research results. The findings revealed that in evaluations conducted without a rubric, teachers were limited in their ability to distinguish individual differences and demonstrated low scoring consistency. In contrast, artificial intelligence tools were more effective in distinguishing individual differences and exhibited high consistency. In evaluations conducted using a rubric, scoring consistency increased in both groups, although, as in the first evaluation, artificial intelligence tools demonstrated a higher level of consistency. Regarding the research findings, teachers expressed that individual biases, mood, and professional experiences influenced their scoring processes and emphasized the potential of rubrics and artificial intelligence-supported feedback systems for achieving more consistent results. Artificial intelligence tools, on the other hand, highlighted their independence from subjective factors but stressed the need for more diverse and generalizable datasets to further enhance their evaluation capacities.
The growing presence of artificial intelligence (AI) in higher education has changed the way students approach academic writing. While AI-powered tools offer practical support in generating ideas, organizing texts, and refining language, their increasing use has also raised concerns about the originality and authenticity of students’ written work. This study aims to examine how AI influences students’ authentic academic writing skills and to identify the patterns of dependence that emerge during the writing process. A convergent mixed-methods design was employed by integrating quantitative data from questionnaires completed by 71 third-semester English education students in Makassar, Indonesia, with qualitative evidence drawn from fifteen empirical and conceptual studies published between 2024 and 2025. Descriptive statistics were used to analyze the survey data, whereas thematic synthesis was applied to the literature findings. The results indicate that students rely on AI to varying degrees across different stages of academic writing. Five interrelated forms of dependence were identified, namely dependence on idea generation, language and text organization, revision and editing, cognitive and metacognitive processes, and writing autonomy. However, the findings suggest that AI is not inherently responsible for weakening students’ writing abilities. Instead, the erosion of authentic writing tends to occur when technological assistance replaces the reflective, critical, and self-regulatory processes that are central to academic writing. These findings underscore the importance of developing educational practices that encourage students to use AI responsibly while maintaining intellectual ownership and academic integrity.
Muhammad Yahrif, Suharti Sirajuddin, Muhamad Khaedar· Al Qodiri : Jurnal Pendidika...· 0 citations
The results revealed relevant differences between the analyzed papers: texts produced in 2023 showed greater stylistic variation, the presence of authorial markers, and irregularities typical of human writing, whereas texts from 2025 presented a higher concentration of indicators associated with linguistic standardization, structural uniformity, and a reduction of individual markers of authorship.
Gabriela Pereira da Silva, I. Rhuan, G. Tardo et al.· 0 citations
The research results show that gender differences should be considered in the integration process of AI technologies in fine arts education, and that female students, in particular, should be supported in their use of technology.
Seyda Misman, O. Ozturk, Mevlut Unal et al.· International journal of tec...· 0 citations
The rapid development of artificial intelligence (AI) has introduced new possibilities for transforming educational assessment processes. Among these developments, AI-assisted grading systems have attracted increasing attention due to their potential to improve efficiency, consistency, and scalability of student evaluation. The present study examines the role of artificial intelligence in student grading by comparing AI-generated scores with human teacher evaluations and by exploring teachers’ perceptions regarding the use of AI in educational assessment. The research adopts a quantitative comparative design. Student-written responses were independently evaluated by teachers and AI systems, and the resulting scores were statistically analyzed to examine the level of agreement between the two grading approaches. In addition, a structured questionnaire was administered to teachers to investigate their attitudes toward AI-assisted grading. The findings indicate that while some AI systems produce scores comparable to human evaluators, others exhibit statistically significant differences, highlighting variability across models. Furthermore, AI systems were found to produce more consistent grading outcomes in relation to the corresponding human evaluators. Nevertheless, teachers recognized the potential of AI to reduce the time required for assessment tasks. However, concerns related to fairness, transparency, and the interpretation of complex student responses remain important considerations. Overall, the results suggest that artificial intelligence can effectively support educational assessment when implemented within hybrid evaluation models that combine automated analysis with human pedagogical oversight.
K. Papageorgiou, Christos Pierrakeas· Education sciences· 0 citations
This study aims to examine pre-service teachers’ attitudes toward artificial intelligence (AI), their competencies in using AI, and their views on its educational implications. The study adopted a convergent mixed-methods design, where quantitative and qualitative data were gathered at the same time. The quantitative component included 432 pre-service teachers from a state university in Türkiye and was conducted using validated scales measuring attitudes and competencies. For the qualitative component, participants responded to open-ended questions, and their answers were examined through content analysis. The findings revealed a significant positive relationship between attitudes toward AI and AI usage competencies. Significant differences were found by gender and department, whereas no differences were observed across grade levels. Qualitative findings indicated that pre-service teachers perceive AI as a supportive tool that enhances instructional efficiency, while also expressing concerns about overreliance, ethical issues, and potential negative effects on critical thinking. The findings highlight the need to align teacher education programs with AI-related competencies. It is recommended that AI-focused pedagogical and ethical training be systematically integrated into teacher education curricula and higher education policies.
Özge Canoğulları· Artvin Çoruh Üniversitesi Ul...· 0 citations
This study analyzes teachers’ prior knowledge of Artificial Intelligence (AI) technology and their perceived role of AI in developing teaching materials for bilingual early childhood education. The study employed a quantitative approach with a descriptive research design. The participants consisted of 10 teachers at Sun Global School and Daycare in Pekanbaru, selected using total sampling. Data were collected using a structured closed-ended questionnaire measured on a five points Likert scale. The data were analyzed using descriptive statistics, including percentage distributions, mean scores, and standard deviations. The findings indicate that teachers have a fairly good foundational understanding of AI technology and perceive AI as beneficial for developing teaching materials. The overall perceived role of AI in teaching material development reached 79.2%, categorized as very high. Teachers perceived AI as useful for generating creative ideas, accelerating the development of teaching materials, and supporting the creation of more engaging and interactive learning media. However, teachers also reported technical challenges and a need for further training in AI use. These findings imply that bilingual early childhood education institutions should provide targeted AI literacy and professional development programs focusing on generating, evaluating, and adapting AI assisted teaching materials according to children’s developmental characteristics and bilingual learning contexts. Institutional support through adequate technological infrastructure and continuous teacher mentoring is also needed to ensure the pedagogically appropriate use of AI.