This research explores the innovative integration of OpenAI’s GPT-3.5 within a university-level English as a Foreign Language (EFL) writing course, illustrating a novel approach to academic instruction. The course follows the ADDIE instructional design model, encompassing five systematic stages: analysis, design, development, implementation, and evaluation. This model serves as the backbone of the course structure, ensuring a comprehensive educational experience. The incorporation of the Technological Pedagogical Content Knowledge (TPACK) framework in this course facilitates the effective integration of GPT-3.5 by enabling instructors to align advanced AI capabilities with appropriate pedagogical strategies, thereby enhancing the learning experience. TPACK guides educators in applying GPT-3.5’s features in a manner that is contextually relevant and pedagogically sound, ensuring the technology’s use complements the course content. The findings from this research are significant. They reveal that GPT-3.5 addresses three fundamental challenges often encountered in academic writing courses. Firstly, it enhances efficiency by providing immediate feedback and generating content ideas, accelerating the writing process. Secondly, GPT-3.5 ensures cohesive organization within students’ writing, guiding them to structure their thoughts more logically. Lastly, it serves as a reliable substitute for traditional peer reviewers, offering critical and objective feedback that students can use to refine their drafts. As students engage with AI, they enter a dynamic partnership. This collaboration with GPT-3.5 fosters critical thinking and empowers students to develop a distinctive writing voice. Through this interaction, students are not merely passive recipients of knowledge but active participants in a learning process that is augmented by cutting-edge technology. This study not only provides insight into the potential of AI-augmented academic writing but also highlights GPT-3.5’s role in promoting writing proficiency. It demonstrates that the application of AI in education can enhance the learning experience without compromising the individuality of student expression.

The purpose of this study was to examine the overseas internship experiences and determine whether they helped to enhance the intended English learning outcomes for students who will be working in the service industry. Seventeen entries of reflective journals written by six students who interned overseas were analyzed. The participants reported their frustrations in language learning and described how these experiences could complement their overall career development in the future. The students identified a gap between everyday language needs in the industry and their language training received in Taiwan. The results indicated that the participants thought the English courses in Taiwan have disproportionately emphasized reading or writing skills, whereas speaking and listening were in high demand in the workplace, particularly given the difficulties occurring in cross-cultural communication. The analysis also indicated that the participants’ oversea working experiences positively affected their English learning motivation. These experiences also helped the participants notice differences in interactions with people from various linguistic backgrounds. English learning in Taiwan is traditionally embedded in English as a foreign language (EFL) classrooms that are reading/writing-centered and examination-oriented. The course content should be revised to improve students’ oral communication skills for the workplace.

In modern educational technology, the digital competencies necessary for effective English language instruction remain relatively unexplored. This research addresses this knowledge gap by examining a graduate-level course designed to prepare prospective EFL student teachers with the essential skills to create digital teaching resources. Grounded in the SAMR framework, this investigation aims to introduce a new pedagogical design reflecting the progression of digital evolution. The primary goal is to determine the overall effectiveness of this structured approach in enhancing graduate students’ digital proficiency and instructional capabilities. By utilizing the SAMR model, the study hopes to establish a standard for assessing technology integration in pedagogical training, thereby improving the digital skills of upcoming EFL instructors across diverse technological platforms. The research narrative is presented across two interconnected academic settings: a graduate module emphasizing practical expertise in developing digital assets and a parallel online undergraduate English module for implementing these digital materials. The findings highlighted both the transformative potential of digital tools and the challenges in their adoption, emphasizing the necessity for training that integrates both technical aspects and pedagogical principles. This study provided strategic insights for curriculum developers in teacher education and offered guidance for future English instruction.

The purpose of this paper is to show how the basic Topic-Comment ordering pattern of the Hakka can be accounted for by the constraint-based optimality theory. Part of the linguistic data used in this paper is adopted from Xu (2002), while those examples presented to show syntactic tests are created by the author. These sentences have been further checked and confirmed by a native speaker of Hakka. This paper proposes an Optimality Theoretic (OT) model that takes into account both syntactic and semantic considerations. It shows that semantic information comes into play successively at different points of OT grammar. First, integrating semantic information into the schema of OT syntax works precisely to describe the Hakka topic-initial sentence pattern. The alignment constraints incorporate information about the semantically defined topic and comment constructions into the constraint design, which interacts with other markedness constraints to filter linguistic constructions during production. Second, semantic constraints are formed to further evaluate form-meaning pairs during the process of interpretation. In this aspect, semantic notions including contrastiveness and markedness are incorporated into the theoretical plan with the purpose of pairing syntactically well-formed sentences with appropriate meaning. The paper successfully presents an optimization model illustrating how syntax and semantics cooperate to pair meanings with linguistic constructions in forming linguistic expressions.