廖容面试准备 | 现代数学启蒙

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一、先给你一句“定心丸”

以你的背景，只要群面不翻车，6298 是“合理录取区间”。
群面不是来“刷掉你”，而是用来判断三件事：

你会不会合作

你会不会思考

你会不会 表达而不压人

二、6298 群面 ≠ 普通港校群面（这是关键）

招生官在群面中不看：

谁说话最多

谁英语最花

谁最 aggressive

招生官真正打分的维度（非常重要）：

维度	权重	他们在看什么
思维质量	⭐⭐⭐⭐	你说的是否有逻辑、有结构
合作意识	⭐⭐⭐⭐	你是否推动“团队前进”
表达清晰度	⭐⭐⭐	是否让别人听懂
领导潜质	⭐⭐⭐	是否自然引导，而非抢话
学术气质	⭐⭐⭐	是否像“未来研究型学生”

三、6298 群面常见题型（你一定会遇到）

题型 1️⃣：AI + 社会议题（最高频）

例如：

“Should AI be allowed to make decisions in education / healthcare?”

“Is algorithmic fairness more important than efficiency?”

“How should society regulate AI?”

👉 这是 6298 的主场，不是陷阱

题型 2️⃣：情境决策型（次高频）

例如：

“Your team is designing an AI system, but data is biased. What do you do?”

“Limited budget: accuracy vs ethics, which do you choose?”

题型 3️⃣：开放式合作任务

例如：

给一个问题

20–30 分钟

最后要一个 group conclusion

四、你在群面中的「最佳角色定位」（非常重要）

你最优解不是“leader”，而是：👉「自然型引导者（Facilitator）」

为什么？

你数理 + AI 强

英语表达稳

思维结构好

📌 招生官最喜欢的角色就是：

“她不是最吵的，但她让讨论变得更清楚了。”

五、你应该“怎么说话”（直接给你模板）

① 开局（前 3 分钟）

不要抢第一个，也不要沉默。

黄金开局句式（任选）：

“Maybe we can first clarify what the key issue is before deciding.”
“I think there are two dimensions here: technical feasibility and social impact.”

📌 这类话 =

直接给你加“成熟度分”

② 中段（你最重要的发力点）

你要做三件事：

✅ 1. 结构化讨论

“So far, we’ve talked about efficiency. Maybe we should also consider fairness.”

✅ 2. 接住别人的话

“I agree with what X said about accuracy, and I’d like to add another perspective…”

✅ 3. 提供“桥梁式观点”

“Technically this is possible, but socially it could raise concerns such as…”

📌 这一步非常像 6298 的真实学习方式

③ 收尾（隐藏加分点）

如果没人总结，你可以说：

“Shall I try to summarise our main points?”

然后用 30 秒结构化总结：

“We generally agree that AI can be useful, but it should be applied with transparency, human oversight, and clear ethical boundaries.”

📌 这一步：

招生官会在表格里给你打“leadership + clarity”

六、你【绝对要避免】的 5 个雷区

❌ 抢话 / 打断

❌ 长时间 monologue

❌ 只谈技术，不谈人

❌ 一直说 “I agree” 没有新内容

❌ 完全不说话（这是唯一可能直接减分的）

七、6298 群面“隐形加分点”（很多人不知道）

1️⃣ 用“条件式语言”，而不是绝对判断

❌ AI should replace teachers
✅ AI can assist teachers under certain conditions

2️⃣ 主动提“human-in-the-loop”

“I think human oversight is essential.”

3️⃣ 承认复杂性

“This is not a simple yes-or-no issue.”

📌 这些话 = 6298 DNA

八、你现在该如何准备（7 天高效版）

Day 1–2

准备 3 个 AI + 社会议题

每个议题想清楚：

技术面
伦理面
折中方案

Day 3–4

用英文 1 分钟表达观点

练“先结构，再观点”

Day 5

模拟群面（哪怕自己假想）

练 接话 + 总结

Day 6

背熟 10 句 高频群面万能句

Day 7

放松

群面不是辩论赛，是合作展示

九、对你非常重要的一句判断

你在 6298 群面中，不需要“表现得很厉害”，只需要“表现得很像未来的 6298 学生”。

1. Please introduce yourself briefly.

Model Answer：

My name is Liao Rong, and I am currently a Year 12 student at Changsha Concordia Craig School, following the CIE A-Level curriculum.

I have a strong academic focus on mathematics, computer science, and the natural sciences, and I am particularly interested in how mathematical thinking and data-driven methods can be applied to artificial intelligence.

Throughout high school, I have actively pursued academic challenges through international competitions, research-oriented projects, and interdisciplinary learning, which gradually shaped my aspiration to study AI and Data Science at the undergraduate level.

2. What courses have you taken in high school? What subjects are you good at?

Model Answer：

In high school, I have taken CIE A-Level Mathematics (9709) and Further Mathematics (9231), and I am currently studying Physics, Chemistry, and Computer Science. I am also completing an EPQ, where I focus on independent research and academic writing.

Among these subjects, I am strongest in mathematics and problem-solving-oriented disciplines. I particularly enjoy pure mathematics, probability, statistics, and algorithmic thinking, as they allow me to reason rigorously and abstractly.

At the same time, physics and computer science help me connect theory with real-world applications, especially in areas related to modeling, optimization, and computation.

3. What activities have you involved in during high school years?

Model Answer：

My extracurricular activities mainly focus on academic competitions, research-oriented learning, and leadership experiences.

I have participated extensively in international mathematics, physics, chemistry, and computer science competitions, such as AMC, AIME, UKMT, USACO, and Physics Bowl.

In addition, I served as the team captain of my school’s NSL robotics team, where I was responsible for strategic planning, algorithm design, and team coordination.

Beyond competitions, I also enjoy self-initiated learning, such as exploring programming projects and reading about recent developments in AI and data science.

4. What are your achievements during high school years?

Model Answer：

Academically, I have achieved strong results across multiple disciplines.

I obtained A* in both CIE Mathematics and Further Mathematics, with 242 out of 250 in Further Mathematics. I also achieved Distinction in BMO Round 1, Gold in the UK Senior Mathematical Challenge, and qualified for AIME twice.

In science competitions, I received Gold in the Physics Bowl, Global Silver in the UK Chemistry Olympiad, and Distinction in the Canadian Euclid Contest, ranking first in my school.

In computer science, I earned USACO Bronze, which strengthened my foundation in algorithmic problem-solving.

These experiences reflect not only academic ability, but also consistency, perseverance, and intellectual curiosity.

5. Why do you want to study at HKU over other universities?

Model Answer：

HKU strongly appeals to me because of its academic reputation, interdisciplinary culture, and global perspective.

Unlike many universities that separate engineering and humanities, HKU actively encourages cross-disciplinary education, which I believe is essential for the responsible development of AI.

In addition, HKU’s strong connections with research institutes, industry, and the Asia-Pacific region provide an ideal environment for studying AI and data science in a real-world, socially relevant context.

I also value HKU’s emphasis on critical thinking, ethics, and societal impact, rather than purely technical training.

6. Why do you want to study AI and Data Science at HKU? Why Arts + Engineering?

Model Answer：

I chose the BA & BEng in AI and Data Science because I believe that AI is not only a technical discipline, but also a social one.

The engineering component equips me with rigorous training in algorithms, machine learning, and data systems, which are essential for building reliable AI models.

At the same time, the arts component allows me to study ethics, human behavior, and social structures, helping me understand how AI systems affect individuals and society.

This combination aligns perfectly with my long-term goal: not just to develop advanced AI technologies, but to ensure that they are fair, interpretable, and socially responsible.

7. A contemporary AI issue that concerns you.

Model Answer：

One issue that concerns me is the use of AI in automated decision-making systems, such as hiring, credit scoring, and university admissions.

Technically, these systems often rely on historical data, which may already contain biases. If such data is used without careful preprocessing or fairness constraints, the model may amplify existing social inequalities.

From a societal perspective, this raises concerns about transparency, accountability, and trust, especially when individuals are affected by decisions they cannot easily challenge or understand.

This issue highlights the importance of combining technical expertise with ethical awareness, which is exactly why I value HKU’s interdisciplinary approach.

8. A fascinating concept or project you learned.

Model Answer：

One concept I found particularly fascinating is probability distributions and statistical modeling, especially how they are used to make predictions under uncertainty.

Through mathematics competitions and further mathematics, I learned how random variables, expectation, and variance form the foundation of many machine learning algorithms.

This helped me realize that AI models are not “black boxes,” but systems built upon mathematical assumptions and statistical reasoning.

Understanding this changed how I view AI—from something mysterious to something analyzable, improvable, and accountable.

9. Can you give an example of algorithmic bias?

Model Answer：

A common example of algorithmic bias occurs in facial recognition systems, which often perform less accurately on certain demographic groups.

This happens because the training data is not sufficiently diverse, leading the model to optimize performance for overrepresented groups.

To mitigate this, developers can improve data diversity, apply fairness-aware algorithms, and conduct regular bias audits.

More importantly, interdisciplinary collaboration is needed so that technical solutions are guided by ethical and social considerations.

10. What are your future career goals?

Model Answer：

In the long term, I hope to work in AI research or applied data science, focusing on building systems that are both technically robust and socially beneficial.

This program will provide me with a strong foundation in engineering skills, statistical reasoning, and ethical thinking, allowing me to pursue careers in AI research labs, technology companies, or interdisciplinary graduate studies.

Ultimately, I aim to contribute to AI development that improves decision-making while respecting human values.

11. What personal strengths make you a good fit?

Model Answer：

I believe my strengths lie in strong analytical ability, intellectual curiosity, and interdisciplinary thinking.

My background in mathematics has trained me to think logically and precisely, while my exposure to multiple disciplines has taught me to approach problems from different perspectives.

I am also highly self-motivated and comfortable with academic challenges, which is essential for a demanding dual-degree program like this one.

12. Do you have any questions for us?

Model Answer（推荐必问）：

Yes, thank you.

I would like to ask how HKU supports students in this program to engage in research or industry projects early on, especially those related to ethical AI or interdisciplinary applications.

下面我给你一套 「群面高频 + 安全 + 高级」的经典英文句型库，全部是港校（尤其 HKU / HKUST / CUHK）群面里“用一次就加分”的表达，而且不会抢话、不会冒犯、不会显得装。

我会按【功能】分类，你可以直接背“粗体句”，其余作为替换。

一、开场 & 建立存在感（不抢话）

✅ 表示参与意愿

“I’d like to share a brief thought on this.”

“If I may add something here…”

“I agree with what has been said, and I’d like to build on that.”

👉 适合第 1–2 次发言，安全、不突兀

二、结构化发言（群面最加分）

✅ 表示你在“搭框架”

“Maybe we can look at this from three perspectives.”

“I think this issue can be broken down into two main parts.”

“Perhaps we can start by defining the problem first.”

👉 面试官会自动给你贴标签：clear thinker

三、表达观点（理性、不绝对）

✅ 高级但不冒险

“From my perspective, one key consideration is…”

“I tend to think that…, although there are limitations.”

“One possible approach could be…”

❌ 避免：I strongly believe, This is obviously wrong

四、同意他人（刷团队分）

✅ 同意 + 升级

“I agree with [Name]’s point, especially regarding…”

“That’s a very interesting point, and I’d like to extend it further.”

“Building on what was just mentioned…”

👉 一定要点名对方，这是港校非常吃香的行为

五、温和不同意（高难但高分）

✅ 安全反驳模板

“I see your point, but I have a slightly different view.”

“That’s a valid argument, though we might also consider…”

“I partially agree, but I think there’s another side to this.”

👉 说完后一定给理由，否则显得空

六、拉回跑偏讨论（隐藏王炸）

✅ 群面 MVP 句型

“Maybe we can refocus on the original question.”

“Just to bring us back to the main issue…”

“Given the time limit, perhaps we should prioritize…”

👉 面试官内心 OS：

“这个人有 leadership”

七、时间管理 & 总结（极其加分）

✅ 主动收尾

“We’re running a bit short on time, so I’ll summarize briefly.”

“To wrap up, we seem to agree on three key points.”

“In summary, our discussion suggests that…”

👉 群面最后 1 分钟说出这句话，直接封神

八、AI / Data Science 群面专用句（你的优势区）

✅ 技术 + 社会视角

“From a technical perspective, this is feasible, but socially it raises concerns.”

“Data-driven solutions are powerful, but they depend heavily on data quality.”

“We should consider both algorithmic efficiency and ethical implications.”

👉 这类句子非常符合 HKU BA & BEng 项目气质

九、被打断 / 插不上话时（救命句）

“Could I briefly add one small point?”

“Just one quick comment, if that’s okay.”

十、群面“绝对不要说”的句子（重要）

❌ “I completely disagree.”

❌ “That’s not correct.”

❌ “Obviously…”

❌ “As the leader, I think…”

👉 港校非常反感 aggressive dominance

🔥 给你一个「万能 20 秒群面模板」（直接背）

“I agree with the previous point. Building on that, I think this issue can be viewed from two perspectives: the technical feasibility and the social impact. From a data science standpoint, the solution is possible, but we should also consider ethical and long-term implications. Given the time, I think this could be a balanced direction for our discussion.”

🎯 建议你这样用：

背 15–20 句，不是全部

每场群面 说 3–4 次就够

至少一次：

接别人
拉结构
或总结