Profile · HKU BSocSc Applicant
Tianshi Rao: AI, Education and Opportunity Equity through Social Science
I am an A-Level student at Wuhan Britain-China School. In summer 2025 I joined the HKU Social Sciences Summer Programme on AI and Society, led my team through a study on AI, education and resource equity, and received the Best Project Presentation Award. Since then my question has been: does technology narrow or widen educational inequality? From November 2025 to June 2026 I founded and led an AI volunteer teaching team across three schools, observing, hypothesising and revising in real classrooms to ask what conditions turn AI into a capability amplifier rather than an inequality amplifier.
HKU Summer Programme
July 2025 — HKU AI and Society; led quantitative analysis, won Best Project Presentation Award, and set my direction on AI, education and opportunity inequality.
Three Teaching Projects
Zhashan Middle School (53) · Xian nv shan Primary (200) · Luxiang Jianhe Branch (50): from access to AI, to gaps in family support and guidance, to a teach-first-then-use-AI intervention.
Quantitative Training
Using 935 observations, STATA and OLS to study returns to schooling: one more year of education raises wages by about 4.3% — learning to turn observation into testable hypotheses.
Curriculum Vitae · HKU BSocSc Applicant
Tianshi Rao / 饶天石
Social Sciences Applicant · AI, Education and Opportunity Inequality
Email: raotianshi2027@163.com
Location: Wuhan, China
Education
Wuhan Britain-China School · A-Level
A-Level subjects: Mathematics, Further Mathematics, Economics, Physics.
Academic Research
Returns to Schooling and Wage Income · Independent Research
Studied schooling and wages with a 935-observation dataset; self-taught STATA and basic econometrics to run descriptive statistics and OLS, adding experience, weekly hours, IQ and other controls stepwise.
The schooling coefficient is about 0.043 and significant at 1%: one more year of schooling raises wages by about 4.3% on average. Discussed education investment and the socioeconomic meaning of wider access through human capital theory.
Academic Enrichment
HKU Social Sciences Summer Programme 2025 — AI & Society
The University of Hong Kong · Best Project Presentation Award
Interdisciplinary study of AI and public services, generative AI and information flows, social shaping of technology, positive youth development, and society-environment issues; hands-on recommender systems and sentiment analysis in the AI in Action workshop.
Led the team final presentation on AI, education and resource equity and took charge of quantitative analysis; won the Best Project Presentation Award. Inspired by Dr Ben Law on AI and positive youth development to focus on the ethical boundary of technology in adolescent growth.
AI and Machine Learning Pioneers Summer School · University of Oxford
Dept. of Economics & Saïd Business School
Studied neural networks, deep learning, large language models, tokenization and attention; explored AI impacts on labor markets, industrial organization, competition and governance; led data and logic checks in a team investment challenge on AI infrastructure — energy, grids, chips, cloud and data centers.
Leadership & Community Engagement
AI Volunteer Teaching Team · Founder & Team Leader
Wuhan · 5 founding members · 3 outreach sessions, ~5 hours total
Owned curriculum design, teaching, logistics, team roles and school liaison; used fieldwork as a lens on unequal educational opportunity and divergent AI use. The three sessions form an observe–hypothesise–revise chain; see the AI & Society page and profile for details.
Zhashan Middle School AI Outreach · Caidian, Wuhan
Taught AI-assisted self-directed learning to about 53 students (~145 min). AI offered low-cost explanation and feedback, yet some students copied answers directly — surfacing a guidance gap beyond access: when to use AI, how to verify, and when independent thinking is required.
Xian nv shan No.2 Primary School AI Outreach · Hanchuan
Second session for about 200 students. The same tools yielded different gains depending on devices, connectivity, family support and parental digital literacy — forming the hypothesis that AI learning gains are shaped jointly by technology, family resources and use environments.
Luxiang Jianhe Branch AI Outreach · Hongshan, Wuhan
Third session for about 50 students with a revised pedagogy: think independently first, then use AI only for expression, organisation and feedback. With thinking preserved, students from all backgrounds used AI more effectively — shifting my question to what conditions make AI a capability amplifier.
School Leadership & Activities
An Zhi Academy · Operations Member → Head of Event Planning
Co-organised events for ~468 members; ran open-day and sports-day charity sales; interviewed off-campus teaching volunteers; joined the 2025 Mid-Autumn visit to critically ill children at Tongji Hospital (logistics, headcount, recruitment).
Politics & Economics Society · Academic Member
Designed economics quiz questions for society day; completed a report on The Economist themes.
Small Animals Society · Active Member
Joined two charity sales and one New Year event; designed promotional slogans.
Service · Honours · Skills
Community Service
UNICEF China monthly donor (June 2024 – present) with a two-year certificate, supporting children in need.
Technical Skills
STATA: descriptives, OLS and controls; Python: basic coding, pseudocode, simple text and numeric data handling.
Languages
IELTS Academic 7.0 (L 7.0 · R 8.5 · W 6.0 · S 6.0, CEFR C1) · Mandarin: Native
Honours & Awards
- AMC 10 (2024) — Certificate of Distinction (Top 5%), First Place
- AMC 12 (2025) — Top 10%, AIME Qualification
- AIME Qualification — 2025 / 2026
- Euclid Contest (2026) — Certificate of Distinction (Top 25%)
- Hypatia Contest (2026) — Certificate of Distinction (Top 25%)
- HKU Summer Programme (2025) — Best Project Presentation Award
Research Interests
AI & Society
AI & Society Initiatives
Documenting outreach and research on AI, learning and educational equity.
August 1, 2025
Rao Tianshi Leads Volunteer Recruitment Drive
Using ¥10,000 of his own savings to bring warmth to children in need and inspire more people to participate in public service.
Learn MoreNovember 21, 2025
Zhashan Middle School: How AI Can Help Us Learn
Fifty-three ninth-graders in 8 groups completed discussions and poster showcases, learning to turn AI into a learning tool rather than a shortcut for thinking.
Learn MoreJanuary 9, 2026
Xian nv shan Primary: Mastering Learning in the AI Age
Some 200 pupils in one auditorium: from thinking clearly before asking AI to seeing through its flattery — a big lecture on learning and the mind.
Learn MoreResearch · Independent Study, Feb 2025 – Aug 2026
How Years of Schooling Affect Wage Income
Testing human capital theory with 935 observations, STATA and OLS: one more year of schooling raises wages by about 4% on average.
1. Introduction
The core question: does years of schooling significantly affect personal wage income? Do more years in school really mean higher earnings? From an economics perspective, education is a human capital investment, and this study tests that theory with data.
Using 935 observations, OLS regressions and STATA, the analysis adds experience, weekly working hours, IQ and other controls stepwise for robustness. Schooling has a significant positive effect: each additional year raises wages by about 4%, stable across specifications.
This project taught me that education is not only knowledge accumulation but a long-term economic investment with a lasting effect on future income.
2. Background & Literature
Human capital theory treats education as a core component of human capital: schooling raises knowledge and skills, hence productivity, and firms pay higher wages for more productive workers.
On one hand, more educated workers tend to have stronger professional and learning abilities and create more value. On the other, signalling theory argues that even if schooling did not raise productivity directly, a higher credential still signals ability to employers and commands higher pay. This study tests the positive schooling–wage link empirically.
3. Data & Variables
The data contain 935 observations estimated by OLS. Mean schooling is about 13.5 years (min 9, max 18) and mean monthly wages about $976: most respondents finished high school and some went on to higher education.
| Variable | Definition |
|---|---|
| wage | Monthly wage income (USD) |
| educ | Years of schooling (years) |
| exper | Work experience (years) |
| hours | Weekly working hours |
| IQ | IQ test score |
4. Results
Regressing log wages on schooling first: the schooling coefficient is about 0.043 and significant at 1%. Because the dependent variable is in logs, this reads as roughly a 4.3% wage gain per additional year of schooling.
Adding experience keeps the schooling coefficient positive and significant, so the effect is independent of tenure; experience itself is also positive. Adding IQ, marital status, urban residence and further controls leaves the coefficient stable at around 4% — a robust result.
Intuition: four more years of schooling ≈ 16% higher wages.
Schooling builds professional knowledge, technical skill, problem-solving and adaptability that employers reward — education as a sound long-run investment.
5. Discussion & Conclusion
Conclusion: schooling significantly raises wages by about 4% per year, robust to controls. For individuals, staying in school longer pays when feasible; for society, broader access lifts overall productivity and growth.
Limitation: schooling may correlate with unobserved factors such as family background; richer data and models could sharpen the estimate. Overall, the study supports clear economic returns to education.