http://hdl.handle.net/20.500.12323/57 2026-04-04T03:12:26Z 2026-04-04T03:12:26Z Giray, Louie Alkhaqani, Ahmed Kamaruddin, Nurliana http://hdl.handle.net/20.500.12323/8236 2026-01-23T08:23:17Z 2025-01-01T00:00:00Z

Title: Navigating the Role of AI in Research in the Global South: A Collective Autoethnography From Researchers in the Philippines, Iraq, and Malaysia Authors: Giray, Louie; Alkhaqani, Ahmed; Kamaruddin, Nurliana Abstract: This paper investigates the transformative impact and ethical dilemmas of integrating artificial intelligence into the workflows of researchers from the Global South, specifically the Philippines, Iraq, and Malaysia. Through collective autoethnography, the authors analyze how AI tools function as both equalizers and disruptors in resource-constrained academic environments. The findings reveal that AI significantly enhances productivity by dismantling language barriers for non-native English speakers, streamlining literature searches, and democratizing access to global scholarship. However, these benefits are accompanied by profound challenges, including the risk of over-reliance, the proliferation of AI hallucinations, and the potential erosion of critical thinking skills. The authors confront the tension between efficiency and intellectual integrity, grappling with the existential question of whether reliance on AI reduces scholars to mere prompt engineers. Furthermore, the paper highlights how algorithmic bias and infrastructure disparities exacerbate the digital divide within local academic communities. The paper concludes that while AI offers unprecedented opportunities for Global South researchers, it requires a shift toward critical AI literacy and ethical governance to prevent the widening of existing knowledge gaps. This study calls for a human-centric approach where AI serves as a support mechanism.

2025-01-01T00:00:00Z İpekdal, Umut Subaşı, Gonca http://hdl.handle.net/20.500.12323/8235 2026-01-23T08:18:47Z 2025-01-01T00:00:00Z

Title: Factors Influencing University English Instructors’ Use of Generative AI in Türkiye: A UTAUT2 Perspective Authors: İpekdal, Umut; Subaşı, Gonca Abstract: This quantitative study investigates the determinants of English language instructors’ acceptance of generative AI tools at the tertiary level, utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework (Venkatesh, Thong & Xu, 2012). Data were collected through a survey of 50 English instructors at a state university in Istanbul, Türkiye, to identify the key factors influencing behavioral intentions and actual use of AI technologies in teaching. The study examined several constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. The findings indicate that perceived usefulness (performance expectancy), ease of use (effort expectancy), and institutional support (facilitating conditions) are significant predictors of generative AI acceptance among instructors. Enjoyment (hedonic motivation) and habitual use also play important roles in technology adoption. These results provide valuable insights into the opportunities and challenges of integrating generative AI into higher education language programs and offer practical guidance for educators and policymakers aiming to foster the adoption of innovative teaching technologies.

2025-01-01T00:00:00Z Önem, Engin Evrim http://hdl.handle.net/20.500.12323/8234 2026-01-23T08:10:29Z 2025-01-01T00:00:00Z

Title: Lost in Ambiguity: AI and the Limits of Processing Turkish Morphology Authors: Önem, Engin Evrim Abstract: Despite rapid progress in natural language processing, artificial intelligence (AI) systems continue to struggle with languages characterized by high morphological complexity, such as Turkish. A central difficulty lies in their limited ability to resolve ambiguity, which is deeply embedded in Turkish through features like flexible word order, extensive case marking, and agglutinative structures. Current AI models, primarily shaped by exposure to massive corpora of performance data, excel at detecting surface-level statistical regularities but fall short of grasping the grammatical principles and semantic nuances that underpin genuine linguistic competence. This reliance on performance over competence makes disambiguation especially problematic, as the systems lack the deep structural awareness required to interpret sentences where multiple readings are possible. To explore these weaknesses, the study tested five prominent AI models, Gemini, Claude, ChatGPT, Deepseek, and Grok, using ten carefully selected ambiguous Turkish sentences. Without being alerted to the ambiguity, the systems frequently generated interpretations that were semantically inappropriate, inconsistent, or distorted by hallucinated content. The findings illustrate how data-driven training alone cannot equip AI with the pragmatic reasoning and world knowledge necessary for accurate interpretation. The paper argues for a shift toward models that integrate richer linguistic theory, enabling AI to move beyond statistical mimicry toward a more human-like capacity for language understanding. Such an approach is vital for developing tools that can handle morphologically rich and ambiguity-prone languages with greater fidelity.

2025-01-01T00:00:00Z Taştan, Kürşat Taştan, Nalan Sabır http://hdl.handle.net/20.500.12323/8233 2026-01-23T08:07:27Z 2025-01-01T00:00:00Z

Title: Artificial Intelligence in Higher Education: A Bibliometric and Science-Mapping Analysis from an Institutional and Management Perspective Authors: Taştan, Kürşat; Taştan, Nalan Sabır Abstract: This article maps the field of artificial intelligence in higher education (HEI-AI) from an institutional and management perspective. We draw on 52,270 peer-reviewed articles and reviews indexed in Web of Science and Scopus between 1959 and 2025. Using the bibliometrix package and its biblioshiny interface in R, we combine descriptive indicators with science-mapping techniques, including co-authorship and co-citation networks, keyword co-occurrence, thematic mapping and thematic evolution. The initial corpus of 94,845 records was cleaned by merging the two databases, removing duplicates and restricting the sample to full-length journal articles and reviews that explicitly address AI in higher education. The results show a long period of slow growth followed by an exponential expansion after 2023, closely aligned with the diffusion of generative AI tools such as ChatGPT. At the country level, China dominates in publication volume, while the United States leads in citation impact. Countries such as France contribute fewer but highly cited papers and function as additional intellectual hubs. Conceptual and thematic analyses indicate a gradual shift towards more technical and data-driven work, centred on artificial intelligence, teaching and learning in tertiary education, and learning analytics, prediction, classification and performance metrics. Interpreted through neo-institutional theory, these patterns point to legitimacy-oriented AI adoption, coercive and mimetic isomorphism, and the growing influence of bibliometric indicators on organisational fields. The paper argues that HEI-AI should be understood as a strategic management and governance issue rather than only a pedagogical innovation, and it outlines implications for institutional AI strategies, policy design and future research on organisational adaptation in higher education. AI is not just technology; it is a process that redefines the institutional structure.

2025-01-01T00:00:00Z