Abstract
Abstract The study fills a gap in the field of applied linguistics, which has not been widely explored, by considering the pedagogical effectiveness and ethical use of AI in higher education. Addressing an underexplored empirical area in applied linguistics, the research builds on recent studies such as Yin and Hanif (2024) and focuses on pedagogical effectiveness and ethical implementation in higher education. A pretest-posttest control group design was used with 148 first-semester undergraduate students. The control group (n = 74) received no intervention, while the experimental group (n = 74) was assigned to a 15-week program using AI via ChatGPT, Claude AI, Meta AI, and Gemini, along with structured ethical practices, including evaluating AI output, paraphrasing, and transparency. The control group was taught in a traditional manner (n = 74). The quantitative results indicated that the AI group had significantly higher scores in both vocabulary and reading comprehension (p < .001). Immediate improvements in vocabulary were observed during the early testing period, but leveled off in the control group in later tests; reading comprehension took time to develop but was maintained. There were also gender differences, possibly due to differences in learning preferences, as males showed greater vocabulary growth whilst females showed greater reading comprehension. The qualitative findings from interviews and surveys based on the TAM indicated that the use of AI would provide greater learner autonomy, reduce cognitive load for learners, and be highly motivated to continue using AI, with ethical usage as a key factor. The results showed that the use of AI in education, guided by ethical principles, can effectively enhance learning outcomes, as evidenced by a 30% improvement in scores related to learning autonomy. The study provides a replicable model for the ethical use of AI in higher education settings in the Global South, which has been applied in Pakistan and can be adapted for other regions. It suggests training faculty to use ethical AI in the classroom through workshops on paraphrase protocols and bias detection, providing better access to AI and infrastructure, and conducting longitudinal studies over 6–12 months following the intervention to measure sustained learning outcomes and retention loss.