1. [Principles of Language Learning and Teaching]

This course will familiarize students with theories, methodologies, issues and practices of language learning and teaching. We will examine theories, principles and models in instructed second language acquisition and will evaluate the applicability of recent pedagogical developments in the language skills and systems for L2 learners in the Korean ELT context.

2. [Language Assessment Using AI]

The goal of this course is to help students build expertise in language assessment. Students will learn the basic theory of language assessment needed to design and analyze language tests. The statistical techniques necessary to analyze language test data will also be covered in the course, and students will learn how to develop and evaluate multiple-choice items, as well as performance assessment items (such as speaking and writing). This course will discuss ways to produce computer-adaptive language tests using artificial intelligence technologies.

3. [Introduction to Corpus Linguistics]

This course introduces Corpus Linguistics as a scientific discipline, blending linguistic theory, quantitative analysis, and data processing. Students will develop foundational knowledge and computational skills for corpus-based research, exploring methodological frameworks, corpus design, data retrieval, and statistical analysis. Hands-on applications include concordances, collocations, keyword analysis, and vector-space modeling for linguistic inquiry.

4. [Research Methods Using Data Coding]

This course introduces students to research methods in applied linguistics including the application of artificial intelligence to language learning. This course covers the procedures necessary for quantitative and qualitative research in applied linguistics and reads examples from journals in applied linguistics. The main goal of this course is to prepare students to be able to conduct their own research studies. Students will also learn basic mathematics and statistical techniques needed to understand how artificial intelligence for language learning works.

5. [AI Basic Programming]

This course introduces text-mining techniques and AI-driven language processing using Python. Students will learn fundamental programming, corpus linguistics, natural language processing, semantic network analysis, topic modeling, and text visualization. Hands-on experience includes working with large language models and managing extensive text data.

6. [Research Methods: Quantitative (CTT)]

This course introduces the core concepts of Classical Test Theory, including the True Score Model, reliability, generalizability theory, validity, test bias and fairness, scale development, and threats to psychometric quality. The goal is to provide students with the skills to conduct robust quantitative research and evaluation in applied linguistics.

7. [Research Methods: Qualitative]

This course is designed to offer a comprehensive introduction to qualitative research methods. Throughout the course, students will develop both the conceptual framework and the technical skills necessary to understand and effectively carry out a qualitative research project. From the initial stages of project conception and design to the final phases of data analysis and write-up, students will gain the knowledge and tools needed to navigate the full research process.

8. [Dissertation I & II]

This course represents the second stage of the dissertation writing process. Students will focus on data analysis, writing the results and discussion sections, and finalizing their dissertation. The course provides support in refining research findings and preparing the dissertation for submission.

1. [Computer-Assisted Language Learning & Teaching]

This course examines the role of computer technology in enhancing language learning and instruction. Students will explore language learning software, multimedia tools, and online platforms while gaining hands-on experience with various computer-assisted language learning (CALL) tools. The course also evaluates the effectiveness of these technologies across different language learning contexts.

2. [AI & Literacy Studies]

This course examines the role of artificial intelligence in literacy studies, with a focus on its impact on language learning, reading comprehension, and text processing. Students will explore AI-driven applications such as automated language assessment, text generation, and speech recognition, assessing their implications for literacy development. Through hands-on activities and critical discussions, the course will also address ethical considerations and the evolving relationship between AI and human communication in educational settings.

3. [Task-based Language Teaching (TBLT)]

This course examines the theory, research, and practice of Task-Based Language Teaching (TBLT), emphasizing its empirical foundations and pedagogical applications. Students will critically engage with research on task design, language learning opportunities, and assessment in TBLT. They will analyze teaching materials, evaluate tasks based on research findings, and apply TBLT principles to their own teaching contexts.

4. [ELT Curriculum Design]

This course is an orientation to the process of second language (SL) curriculum design—planning, development, assessment, and evaluation. You will learn how to apply theory to practice and create effective ELT programs. By the end of the course, you should be able to construct a customized SL learning program that effectively targets one (or several) of the major skills areas (e.g., speaking, reading).

5. [Discourse Analysis]

This course introduces discourse analysis, focusing on how language functions in various communicative contexts. The course will explore a range of linguistic approaches, including Conversation Analysis, Systemic Functional Linguistics, corpus-based analysis, and genre analysis. Students will analyze empirical studies on discourse features like coherence, cohesion, stance, register variation, and classroom discourse.

6. [Vocabulary & Phraseology]

This course explores the acquisition, processing, and use of vocabulary and phraseology in language, with a focus on English. Topics include collocations, formulaic language, and multi-word expressions, as well as second language acquisition, cognition, stylistics, lexicography, and discourse analysis. Students will engage with key texts and recent research using approaches like corpus linguistics, construction grammar, and computational methods.

7. [Natural Language Processing (& Large Language Models)]

This course explores Natural Language Processing (NLP) and Large Language Models (LLMs). The course will cover text analysis, from preprocessing to feature extraction, and apply machine learning, including deep learning models, to understand and generate human language. Students will learn to use tools like NLTK and Hugging Face Transformers. Hands-on projects will include applying NLP to language education.

8. [Second Language Psycholinguistics]

This course explores the cognitive processes involved in second language acquisition. Students will examine topics such as language transfer, error analysis, and the roles of memory and attention. It provides insights into the psychological mechanisms and brain science behind language learning, aiming to foster a deep appreciation for its complexities and the dynamic interplay of cognitive factors.

9. [Sociolinguistics]

This course explores how we use language in our everyday lives, focusing on how social context and identity shape language use, and how this, in turn, affects how others perceive us and influence our experiences. The course examines both theoretical frameworks and empirical studies in sociolinguistics.

10. [Spoken Data Analysis & Technology]

This course focuses on the linguistic description and analysis of oral language, with an emphasis on the technological tools necessary for oral language research and teaching. Students will explore key areas such as phonetics, phonology, and the variability of oral language across different registers and dialects. They will learn how to interpret and utilize spectrograms, waveforms, and fundamental frequency contours, and gain practical experience with software such as PRAAT and relevant Natural Language Processing (NLP) tools. Through practical exercises and critiques, students will gain the skills needed to apply technology effectively in oral language research and pedagogical contexts.

11. [Advanced Quantitative Methods]

This course equips applied linguists with advanced quantitative methods for critiquing research and conducting their own studies. Focusing on techniques such as multiple regression, exploratory factor analysis, and structural equation modeling, the course is tailored for those working with large corpora and technology. Emphasizing practical application over theory, students will use various statistical software packages to perform data analyses relevant to language assessment and applied linguistics research.

12. [Advanced Studies in Corpus Linguistics]

This advanced course in corpus linguistics focuses on the use of large-scale data to investigate language patterns and structure. Students will gain hands-on experience with sophisticated corpus tools and techniques, including semantic analysis, discourse studies, and multidimensional analysis. Emphasizing advanced applications of corpus linguistics, the course will explore how these methods can be applied to diverse linguistic research questions, enabling students to analyze and interpret complex language data.

13. [Advance Studies in Language Test Development & Validation]

This practical course trains students in developing language assessments, covering various task formats such as multiple-choice, short answer, and interviews, as well as rating scales like analytic and holistic. Students will learn test development frameworks and engage in hands-on activities to create their own language assessments. The course also focuses on evaluating and analyzing validity arguments in language testing, helping students to distinguish between approaches to construct definition and validity, and develop plans for validation research.