Use AI wisely. Save your time for the economic thinking that matters.
AI is rapidly changing how we collect data, analyze information, write code, and carry out research. For economics students, the goal is not simply to learn new AI tools, but to understand how to use them to support economic analysis.
AI can assist with many routine tasks, but identifying meaningful research questions, choosing appropriate data and methods, assessing causal relationships, and interpreting results still require solid training in economics.
Let AI assist with execution; let economics guide judgment.
Start with existing Department courses to build data and AI skills, then choose interdisciplinary programs that match your learning goals.
University Core "Information Technology"｜2 Credits
Economics students must meet NDHU's programming competency graduation requirement. Course selection, testing, and credit recognition follow the University's current regulations.
View Programming Competency RequirementCompleting these three Department courses, for a total of 9 credits, fulfills the course requirements of this micro program.
View Official CurriculumTake at least one course from each of the five course groups, for a total of at least 15 credits.
An interdisciplinary option combining programming, data analysis, and practical applications.
View Official CurriculumIncludes an Artificial Intelligence and Big Data Analysis track together with πPBL transdisciplinary project work.
View Official CurriculumTrue AI literacy is not just knowing how to ask AI. It also means knowing what can be delegated to AI, what still requires your own judgment, and how to verify the work AI completes.
Identify the problem and confirm the goals and requirements.
Define clear working rules so that AI-assisted work can be tracked and checked.
Check the outputs, ask for the reasoning, and verify the methods and results.
Start from a research question and work through data acquisition, analysis, visualization, result checking, and presentation of research findings.
Using U.S. inflation and unemployment as a case study, Session 1 uses FRED data and an AI agent to assist with data processing, visualization, OLS estimation, comparisons across periods, sensitivity checks, and presentation creation.
Start from an economic phenomenon and formulate a question worth analyzing.
Assist with finding data sources, understanding variables, and cleaning and transforming data.
Assist with writing, explaining, revising, and checking data-analysis code.
Assist with descriptive statistics, model estimation, comparative analysis, and sensitivity checks.
Check whether the data, code, estimates, and AI-generated interpretations are reasonable.
Present results through charts, text, and slides, and ground the interpretation in economics.
AI can assist with analytical execution, but researchers remain responsible for the research question, data choices, research design, causal judgment, and economic interpretation.
AI is a tool for learning and research, not an absolute authority. Use independent judgment, verify information, and follow course and academic requirements when using AI.
People remain responsible for important judgments.
Do not treat AI as an unquestionably correct source of information.
Avoid entering sensitive information or data that should not be made public.
Disclose AI use appropriately according to course, assignment, and research requirements.
Be alert to bias, unfairness, or discriminatory outcomes that AI may produce.