Abstract
University students are entering a labour market in which knowledge work is being reshaped by artificial intelligence, digital platforms, and rapid skill transformation. In this environment, students do not merely need more information, more applications, or faster access to digital tools. They need the capacity to concentrate deeply, transform information into knowledge, turn knowledge into visible outputs, and convert outputs into employability. This opening article introduces a five-part expert series on deep work for university students through the lens of the Balanced Scorecard. The article argues that deep work should be treated not as a personal productivity trick, but as a strategic learning capability. The Balanced Scorecard provides a practical structure for linking personal resources, stakeholder value, learning processes, and long-term development. Drawing from research on deep work, attention, media multitasking, artificial intelligence adoption, labour-market transformation, and lean enterprise thinking, the article proposes a student-centred framework for building disciplined learning in the age of artificial intelligence. Practical examples from Microsoft and LinkedIn, the World Economic Forum, Van Hien University, Swifthub, InnoX 2026, and lean enterprise training materials are used to connect theory with real situations. The central claim is direct: in an artificial-intelligence-rich world, the advantage of students will not come from using tools faster, but from thinking deeper, learning more deliberately, and building evidence of competence through focused work.
Keywords: deep work, artificial intelligence, Balanced Scorecard, university students, employability, attention management, strategic learning
1. Why This Series Begins With Deep Work
The starting point of this five-part series is simple: students today do not suffer from a shortage of information. They suffer from a shortage of sustained attention. A student can open a learning management system, ask an artificial intelligence tool to summarize a topic, watch a tutorial, read a short post, join a class group, and search for examples within a few minutes. Yet after all of that, the student may still be unable to explain the issue clearly, solve a case independently, or produce a professional piece of work.
That gap is the real problem. The modern student is surrounded by information, but information alone does not become competence. Competence requires concentration, repetition, reflection, feedback, and disciplined execution. This is the reason deep work must be placed at the beginning of the series. Deep work is not merely a study habit; it is the foundation for academic growth, career readiness, and responsible use of artificial intelligence.
Cal Newport described deep work as focused, distraction-free work on cognitively demanding tasks that create meaningful value (Newport, 2016). For university students, this means reading difficult material until it becomes understandable, writing with evidence rather than copying, analysing business cases rather than memorizing slides, and building projects that can stand in front of real users, employers, or academic reviewers.
The deeper argument of this article is that deep work should be managed strategically. Students should not treat focus as a matter of mood or motivation. Focus should be designed, measured, protected, and improved. That is where the Balanced Scorecard becomes useful.
2. The New Context: Students Are Entering an Artificial-Intelligence Labour Market
The context surrounding university education has changed. Artificial intelligence is no longer a distant technological topic. It is already embedded in work, business processes, hiring expectations, and daily knowledge tasks. Microsoft and LinkedIn reported in the 2024 Work Trend Index that 75% of global knowledge workers use generative artificial intelligence at work, while the survey covered 31,000 people across 31 countries (Microsoft & LinkedIn, 2024). The same report also found that 66% of leaders said they would not hire someone without artificial intelligence skills, and 71% said they would prefer a less experienced candidate with artificial intelligence skills over a more experienced candidate without them (Microsoft & LinkedIn, 2024).
The World Economic Forum provides a wider labour-market picture. Its Future of Jobs Report 2025 projects that job disruption will equate to 22% of jobs by 2030, with 170 million roles created, 92 million displaced, and a net increase of 78 million jobs (World Economic Forum, 2025). The report also states that nearly 40% of required job skills are expected to change and that 63% of employers already cite the skills gap as the main barrier to transformation (World Economic Forum, 2025).
The implication for university students is serious. A degree remains important, but a degree alone is no longer sufficient. Students need visible competence. They need the ability to learn quickly, use technology responsibly, solve unfamiliar problems, communicate clearly, work with data, and produce evidence of value. Artificial intelligence increases the speed of work, but it also raises the standard of work. If everyone can generate a first draft, the real difference lies in judgment, depth, verification, originality, and execution.
Therefore, the central educational question is not whether students should use artificial intelligence. The real question is how students can use artificial intelligence without losing their ability to think. Deep work is the discipline that protects thinking in the age of intelligent tools.
3. The Attention Crisis: Not a Moral Failure, but a Design Problem
Many students blame themselves for lack of concentration. They describe themselves as lazy, inconsistent, or undisciplined. This self-judgment is often too shallow. The attention problem is not only psychological; it is also environmental and structural. The digital environment is designed to interrupt. Messages, short videos, notifications, recommendation systems, and social pressure constantly compete for the same limited resource: attention.
Gloria Mark’s research on attention and technology use reports that attention on any screen averages about 47 seconds, while also emphasizing that human focus is changing rather than simply disappearing (Mark, 2023). This matters for education because university-level learning requires holding a concept, argument, dataset, case, or problem in mind long enough to understand its structure. If attention breaks every minute, deep understanding becomes difficult.
The research on media multitasking is also relevant. Ophir, Nass, and Wagner (2009) found that heavy media multitaskers performed worse in several areas of cognitive control, including filtering irrelevant information. For students, this means that studying while constantly switching between chat messages, social media, videos, and artificial intelligence tools is not a harmless habit. It trains the mind to remain at the surface of learning.
This article does not argue that students should reject technology. That would be unrealistic and unnecessary. Instead, students need an operating system for attention. They must decide when technology supports thinking and when it fragments thinking. Deep work is the operating system that separates intelligent technology use from passive digital consumption.
4. Why the Balanced Scorecard Belongs in a Student Conversation
The Balanced Scorecard was originally developed as a strategic management system that helps organizations connect performance measures with strategy across multiple perspectives (Kaplan & Norton, 1992, 1996). In this article, the concept is adapted to students. The goal is not to turn students into machines of measurement. The goal is to help students connect what they do every week with who they are becoming professionally.
Most students manage their development with scattered indicators: grades, attendance, assignments, certificates, extracurricular activities, part-time jobs, and occasional internships. These indicators are useful, but they are often disconnected. A student may have many activities but no clear direction. Another student may have good grades but no portfolio. A third may use artificial intelligence daily but cannot explain how it improves learning quality.
A student-centred Balanced Scorecard can solve this fragmentation. It can translate learning into four connected perspectives: personal resources, stakeholders and career value, learning processes, and growth capabilities. Each perspective asks a different question. Am I using my time and attention wisely? Am I creating value for teachers, employers, users, or communities? Do I have a disciplined process for learning and project execution? Am I developing the capabilities needed for the future?
This is why deep work and the Balanced Scorecard fit together. Deep work provides the discipline of focused effort. The Balanced Scorecard provides the architecture for deciding where that effort should go.
5. The Five-Part Series: A Strategic Learning Journey
The five articles following this opening piece are designed as a structured learning journey. They are not isolated articles. Each article develops one dimension of the same argument: students need to transform focus into measurable development and measurable development into employability.
Article 1, Deep Work as the Foundation of Student Learning, explains why deep work is a core academic and professional capability in the age of artificial intelligence. It shows that deep work is not a romantic idea about quiet study; it is a practical response to distraction, automation, and rising labour-market standards.
Article 2, Managing Attention as a Personal Asset, treats attention as a scarce resource. It applies the resource perspective of the Balanced Scorecard to student life and argues that time, energy, and attention must be invested rather than spent randomly.
Article 3, From Grades to Competence Portfolios, shifts the focus from academic marks to visible evidence of ability. It connects deep work with career readiness and shows why students need project outputs, reflective writing, applied cases, and professional portfolios.
Article 4, Designing Deep Learning Processes, analyses the process perspective. It explains how students can build routines, weekly review systems, project milestones, feedback loops, and disciplined execution habits rather than relying on last-minute effort.
Article 5, Deep Work With Artificial Intelligence, addresses the relationship between students and intelligent tools. It argues that artificial intelligence should extend thinking, not replace thinking. The article introduces a responsible workflow for using artificial intelligence in reading, writing, analysis, and project work.
Together, the five articles form a practical strategy: focus deeply, protect attention, build evidence, design process, and use artificial intelligence responsibly.
6. Lessons From Lean Enterprise Thinking: Students Also Need to Become Lean
The lean enterprise materials provided for this series make an important point: in the age of artificial intelligence, organizations do not win by doing more things. They win by focusing resources on what creates the highest value. The materials argue that modern competition is moving from doing more to doing better, from chasing every opportunity to choosing the right opportunity, and from expansion to focus (Vu, 2026a).
This enterprise lesson applies directly to students. Many students are not failing because they do too little. They are failing because they do too many disconnected things. They join too many groups, follow too many online courses, save too many resources, open too many tools, and commit to too many goals. The result is not excellence. The result is cognitive clutter.
A lean student is not a student who studies less. A lean student is a student who removes low-value activities to concentrate on high-value learning. The lean student asks: Which course matters most for my career direction? Which skill will create the strongest employability signal? Which project can become a portfolio asset? Which digital tool truly improves my thinking? Which activities are only creating the feeling of being busy?
This is a hard discipline. Saying no is more difficult than saying yes. But in both enterprise management and student development, focus is a strategic decision.
7. Real-World Anchors: Van Hien University, Swifthub, and InnoX 2026
A strong university education must connect classroom learning with practice. Van Hien University provides useful examples for this series. In 2026, students in Logistics and Supply Chain Management participated in practical learning at Swifthub, a company operating modern warehousing in logistics and e-commerce. The programme allowed students to observe e-fulfillment, high-rack warehousing, inbound operations, storage, picking, packing, sorting, and order-management technology (Van Hien University, 2026a).
This kind of experience becomes valuable only when students process it deeply. A visit to a company is not learning by itself. It becomes learning when students prepare questions before the visit, observe processes during the visit, connect observations with theory, write reflective analysis after the visit, and convert insights into career competence.
Another example is the InnoX 2026 competition. A student team from Van Hien University and Ho Chi Minh City University of Technology won the Special Prize with the project Braille Connect AI, which aimed to recognize Vietnamese Braille from images and convert it into text and speech for people who are deafblind or visually impaired (Van Hien University, 2026b). This example shows the true purpose of deep work in the age of artificial intelligence: not merely to complete assignments, but to solve human problems through disciplined learning and technological application.
The Faculty of Economics and Management at Van Hien University also reports 26 years of development, seven training programmes, and over 90% of students employed after graduation, while emphasizing applied training and practical experience through real activities and internships (Faculty of Economics and Management, 2023). These data points reinforce the need for a student development framework that connects learning, practice, technology, and employability.
8. A Practical Balanced Scorecard for Deep Work
The opening framework of the series can be summarized in a student-centred Balanced Scorecard. It is not a bureaucratic checklist. It is a practical map for disciplined growth.
9. How Students Can Start: A Five-Step Entry Method
Students do not need to redesign their entire life immediately. The better starting point is simple and measurable.
Step 1: Choose one academic priority. Select one course, project, or skill that matters most for the next four weeks. Avoid choosing five priorities at the same time.
Step 2: Schedule three deep-work blocks per week. Each block should last between 60 and 90 minutes. The task must be difficult enough to require concentration: reading, writing, solving, analysing, designing, or revising.
Step 3: Define a visible output for every block. The output may be one page of analysis, a diagram, a revised paragraph, a set of solved problems, a research summary, or a project decision. If there is no output, the block is incomplete.
Step 4: Use artificial intelligence only after forming an initial view. Students should first read, think, and write rough ideas. After that, artificial intelligence can be used to challenge, organize, compare, or improve the work. This sequence protects original thinking.
Step 5: Review every week. Students should ask: What did I complete? What distracted me? What evidence did I create? What should I stop doing next week? What will I do more deeply?
10. The Central Argument
The central argument of this opening article is that deep work is no longer optional for university students. It is a strategic capability. Artificial intelligence can accelerate surface-level production, but it cannot replace disciplined attention, ethical judgment, practical reflection, and responsibility for final output.
The Balanced Scorecard gives students a way to manage learning like a development system. It prevents deep work from becoming a vague motivational slogan. It asks students to define resources, stakeholders, processes, and capabilities. It also asks them to prove progress through evidence.
For Van Hien University students, the implication is practical. Classroom knowledge must be connected with company experience, project work, technological competence, and personal discipline. The student who can focus deeply, use artificial intelligence responsibly, and produce visible evidence of competence will be better prepared for an uncertain labour market than the student who only consumes information quickly.
In short, the future belongs not to students who merely know more tools, but to students who can think more clearly, work more deeply, and create more valuable evidence of learning.
11. Conclusion
This opening article introduces the foundation of a five-part series on deep work for university students in the age of artificial intelligence. The series is built on one practical belief: students need a strategic system for learning, not just more advice about motivation. Deep work provides the discipline. The Balanced Scorecard provides the structure. Artificial intelligence provides powerful support when used responsibly. Real-world practice provides the testing ground. Together, these elements can help students move from fragmented study to strategic development.
The next article will examine the first major theme in depth: why deep work should be considered a foundational academic and professional capability for university students in a distracted digital environment.
References
Faculty of Economics and Management, Van Hien University. (2023, December 18). Giới thiệu Khoa Kinh tế - Quản trị. https://ktqt.vhu.edu.vn/vi/gioi-thieu-474/gioi-thieu-khoa-kinh-te-quan-tri-3620
Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79.
Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Harvard Business School Press.
Mark, G. (2023). Attention span: A groundbreaking way to restore balance, happiness and productivity. Hanover Square Press. https://gloriamark.com/attention-span/
Microsoft & LinkedIn. (2024, May 8). AI at work is here. Now comes the hard part: 2024 Work Trend Index annual report. Microsoft WorkLab. https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
Newport, C. (2016). Deep work: Rules for focused success in a distracted world. Grand Central Publishing.
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583-15587. https://doi.org/10.1073/pnas.0903620106
Van Hien University. (2026a, May 13). Từ giảng đường đến doanh nghiệp: Sinh viên ngành Logistics và Quản lý Chuỗi cung ứng VHU học thực tế tại Swifthub. https://vhu.edu.vn/vi/tin-tuc-hoat-dong/tu-giang-duong-den-doanh-nghiep-sinh-vien-nganh-logistics-va-quan-ly-chuoi-cung-ung-vhu-hoc-thuc-te-tai-swifthub
Van Hien University. (2026b, May 29). Sinh viên Văn Hiến đạt Giải Đặc biệt Cuộc thi Đổi mới sáng tạo cấp Thành phố InnoX 2026: Vượt giới hạn - Chạm tương lai. https://vhu.edu.vn/vi/tin-tuc-hoat-dong/sinh-vien-van-hien-dat-giai-dac-biet-cuoc-thi-doi-moi-sang-tao-cap-thanh-pho-innox-2026-vuot-gioi-han-cham-tuong-lai
Vu, T. A. (2026a). Tinh gọn doanh nghiệp: Sổ tay hướng dẫn doanh nghiệp tồn tại trong kỷ nguyên AI - Phần 2: Tinh gọn - Bài toán sống còn của doanh nghiệp trong thời đại biến động [Training material]. AI University.
Vu, T. A. (2026b). Tinh gọn doanh nghiệp: Sổ tay hướng dẫn doanh nghiệp tồn tại trong kỷ nguyên AI - Phần 3: 9 trụ cột của doanh nghiệp tinh gọn [Training material]. AI University.
World Economic Forum. (2025, January 7). Future of Jobs Report 2025: 78 million new job opportunities by 2030 but urgent upskilling needed to prepare workforces. https://www.weforum.org/press/2025/01/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/
Nguyen Phuong Duy, Faculty of Economics and Management