Teaching

Courses

Courses at Sogang University in 2026, designed primarily for students in the Division of English and the humanities. Syllabi are available as PDF; schedules are tentative and subject to change.

Fall 2026

ENG2112-01Fall 2026Mon/Wed 10:30–11:453 credits · Open to all undergraduates

AI와 언어학 Linguistics with AI

This course explores the consilience of linguistics and artificial intelligence, examining how human language and its contexts can be understood and reimagined through computational tools. Rather than focusing on AI technology itself, it emphasizes how humanistic insight and creativity can guide the design of products and services people truly need.

Format
Lecture 70% · Discussion 10% · Presentations 20%
Evaluation
Midterm 30% · Final 30% · Presentations 10% · Projects 25% · Participation 5%
Prerequisites
None (basic NLP/AI concepts helpful)
Weekly topics (tentative)
  1. Introduction to AI and Linguistics — AI, NLP, GenAI
  2. Prompt Engineering — Generative AI (lab session, project team building)
  3. Syntax — Dependency Parsing
  4. Principles of Machine Learning (1) — Classification
  5. Principles of Machine Learning (2) — Word Embedding, RNN, LSTM, Transformer
  6. Principles of Machine Learning (3) — Transformer, Tokenization
  7. … semantics, pragmatics, human-centered AI applications, and team project presentations (see syllabus)
Syllabus (PDF)
ENG3510-01Fall 2026Wed/Fri 13:30–14:453 credits

인공지능을 위한 데이터분석 Data Analysis for AI

Designed for students in the English Division and the humanities, this course introduces fundamental experiences in data analysis. Students learn to handle textual data with Python, lower the barrier to coding through vibe coding, run preprocessing and exploratory analysis on large text datasets, and practice prompt engineering as a way of communicating with AI — integrating literary perspectives with data-driven methods.

Format
Lecture 45% · Experiment/Practicum 45% · Presentations 10%
Note
Discouraged for engineering students with coding experience. A paid AI tool (e.g. ChatGPT Pro, Claude Pro) is recommended for projects.
Recommended
Introduction to Linguistics (ENG2003), Linguistics with AI (ENG2112)
Weekly topics (tentative)
  1. Introduction to AI and Vibe coding 1 — Python basics (variables, strings, loops)
  2. Introduction to AI and Vibe coding 2 — Python basics
  3. Data Processing 1 — File I/O, string normalization, regex, tokenization
  4. Data Processing 2 — Sample datasets
  5. Data analysis with Python 1 — Pandas basics, word frequency, simple visualizations
  6. … exploratory text analysis, generative AI and prompt engineering, final projects (see syllabus)
Syllabus (PDF)
STS2026-01Fall 2026Mon 13:30–16:153 credits · 전학년

생성형 AI의 이해와 활용 Understanding and Using Generative AI

인공지능 비전공자를 대상으로 생성형 AI 모델과 다양한 인공지능 기법의 기본 원리를 직관적인 예제로 쉽게 이해하도록 강의합니다. 구체적인 알고리즘 구현이나 실습 대신, ChatGPT·Gemini·Claude 같은 생성형 AI와 AI-Hub 공개 데이터를 활용해 각자의 분야에서 가치를 높이는 팀 프로젝트(창업 계획서, 연구 제안서, 영상·이미지 제작 등)를 수행합니다.

수업방법
강의 70% · 토의/토론 10% · 발표 20%
평가
중간고사 2회 60% · 토론 및 발표 10% · 팀 프로젝트 25% · 참여도 5%
주차별 계획 (변경 가능)
  1. 교과 운영 방안 소개, 인공지능의 개요와 역사 (온라인 강의)
  2. 생성형 AI의 개요: 활용 측면 및 prompt engineering (2–3주)
  3. 기계 학습의 기본 원리: 생성형 AI의 개발 측면
  4. 언어 처리를 위한 신경망 모델: Word Embedding, From RNN to Transformer (6–7주)
  5. 중간고사 1
  6. 정보 검색과 생성형 질의응답
  7. 컴퓨터 비전과 멀티모달 AI
  8. 생성형 AI의 미래 방향: Actionable AI, Agent 모델
  9. AI 혁신 사례 (과학, 의학, 바이오, 법률, 스마트 팩토리, 예술 등) · 중간고사 2
  10. 인공지능 결과물에 대한 비판적 평가와 윤리적 활용
  11. 생성형 AI 활용 최종 프로젝트 프레젠테이션 (14–16주)
수업계획서 (PDF)

Spring 2026

ENGG232ENG5232 (Graduate)Spring 2026Wed/Fri 15:00–16:153 credits · Junior/Senior & Graduate

언어기술을 위한 데이터 중심 AI 개론 Introduction to Data-Centric AI for Language Technology

Understand, evaluate, and improve Large Language Models from a "Data-Centric" perspective. Instead of model architecture or heavy coding, students learn to control AI with prompt engineering and to evaluate AI by constructing their own benchmark datasets, leveraging domain expertise in linguistics, culture, and education. The final goal is a KCI-level paper built on a domain-specific benchmark. Undergraduate and graduate sections are graded separately.

Format
Lecture 30% · Discussion 5% · Experiment 15% · Projects 35% · Presentations 15%
Evaluation
Midterm 15% · Quizzes 10% · Presentations 10% · Projects 40% · Assignments 20% · Attendance 5%
Recommended
Linguistics with AI (ENG2112), Data Analysis for AI (ENG3510), or equivalent
Weekly topics (tentative)
  1. Orientation and AI Literacy — Why Data Matters More Than Models?
  2. LLM Basics & Prompt Engineering — Instruction & Context
  3. Advanced Reasoning & Logic Design — Chain-of-Thought, Tree-of-Thought
  4. Evaluation and Benchmark — LLM Benchmarks, Linguistic Metrics, LLM-as-a-judge
  5. The Shadows of LLMs — Bias and Ethics, Hallucination
  6. … synthetic data generation, benchmark construction, and final research paper (see syllabus)
Syllabus — Undergraduate (PDF)Syllabus — Graduate (PDF)