GEL2026 Technology Entrepreneurship in AI-enhanced Business and National Security
The Complete Study & Assessment Guide · Semester 1, 2026/27GEL2026 Overview
- The Education University of Hong Kong
- Semester 1, 2026/27
- Level 2 course
- 3 credit points
- Coursework assessment
What this course asks you to build
GEL2026 treats technology entrepreneurship as the work of converting advances in science and technology into ventures that solve a real customer problem. The course does not reward a technology-first pitch.
- Assessed by A group commercialisation proposal, a group business-plan presentation and an individual reflective essay.
- Pass conditions Complete at least 9 CEIE contact hours plus evaluations and attend at least 80% of instructor-led classroom sessions. Failure of either condition causes course failure regardless of grades; confirm completion on the course Moodle page.
- Core method Connect a customer problem to an AI-enabled solution, testable value, viable economics and responsible implementation.
- Source conflict The published files disagree on some proposal requirements and the reflective-essay date, so use the live Moodle task settings operationally.
How GEL2026 is assessed
| Component | Weight | Format |
|---|---|---|
| Proposal on technology commercialization (group) | 20% | Investor-facing proposal with executive summary, Lean Canvas, implementation timeline and references |
| 30-min oral presentation of business plan (group) | 30% | Business-plan presentation applying AI or machine learning to customer needs with national-security consideration |
| Reflective essay (individual) | 50% | Reflective practice on an AI-enhanced business case, project communication and wider issues |
The three numeric weights total 100%. The company-information and founding-member list is an unweighted milestone. The course also carries two attendance-based pass conditions outside the numeric total: complete 9 contact hours plus evaluations and attend at least 80% of instructor-led classroom sessions. Failure of either condition causes course failure regardless of assessment grades. Track both requirements on the course Moodle page. The dated schedule gives 3 December 2026 for the reflective essay, while the live submission item is titled with 4 December 2026; use the current Moodle submission setting for the operational deadline. The proposal sources also disagree on group size and total word range, so follow the live task brief used by your class.
Current GEL2026 dates
| Date | Item | Control |
|---|---|---|
| 23 September 2026 | Company information and founding-member list | Published in the dated schedule and Moodle assessment area. |
| 4 November 2026 | Commercialisation proposal | Published in the dated schedule and live submission page. |
| 18 November 2026 | Presentation materials | Published deadline for group presentation materials. |
| 21 November 2026 | CEIE reflection | Published completion date on the CEIE activity page. |
| 3 or 4 December 2026 | Reflective essay | The dated schedule and live submission item disagree; use the current Moodle setting. |
Dates are as published in the dated Semester 1 schedule and live Moodle activity pages. Confirm exact deadlines and submission settings in the live LMS.
What GEL2026 covers
Six chapters move from venture logic and the Lean Canvas through AI business models to proposal writing, national-security constraints, pitching, reflection and the course hurdles.
Technology Entrepreneurship and Venture Logic
customer value, feasibility, viability, defensibility and the evidence chain from invention to ventureFrom a technical possibility to a venture
Technology entrepreneurship joins a customer problem, a feasible solution, a viable business model and evidence that the team can deliver responsibly.
- Invention compared with opportunity
- Customer value and existing alternatives
- Desirability, feasibility and viability
- Revenue, costs and time to proof
- Defensibility and compounding assets
- Compliance and national-security constraints
Customer Problems and the Lean Canvas
early adopters, existing alternatives, value propositions, solutions, channels, metrics, economics and unfair advantageNine boxes, one causal argument
The Lean Canvas helps a team connect early adopters and their problems to an offer, solution, channel, revenue, costs, metrics and unfair advantage. The boxes are hypotheses rather than presentation tiles.
- Early adopters and customer jobs
- Problems without embedded solutions
- Existing alternatives
- Unique value propositions
- Solutions and minimum tests
- Channels and customer acquisition
How AI Learns and Creates Business Value
rule-based and example-based systems, training and inference, classifiers, generative models and venture valueExplain the mechanism at business depth
The course contrasts rule-based systems with example-based machine learning. In the first, people specify decision rules.
- Rule-based and example-based systems
- Labelled training examples
- Weight adjustment and model formation
- Inference on new input
- Classifiers and generative models
- Pain point to AI capability
Building the Commercialisation Proposal
executive summary, one-page canvas, implementation cycles, explicit revenue and cost estimates, references and work allocationThe proposal is an investor decision document
The group proposal combines an executive summary, a concise Lean Canvas with elaboration, an implementation timeline, references and a clear work allocation.
- Investor-facing executive summary
- One-page Lean Canvas
- Customer segments and problems
- Revenue and cost estimates
- Key metrics and unfair advantage
- Build-measure-learn timeline
National Security, Ethics and Legal Constraints
technological interdependence, sensitive capabilities, information rights, accountability, control and lawful venture designConstraints reshape the opportunity
The formal course content connects technology entrepreneurship to emerging and disruptive technology, technological interdependence, cryptocurrency, non-fungible tokens and the legal and ethical implications of venture activity.
- Emerging and disruptive technology
- Technological interdependence
- Sensitive data and capabilities
- Information and property rights
- Accountability and operational control
- National-security education and law
Pitching, Reflection and Course Hurdles
the group business-plan presentation, individual reflective practice, evidence use, attendance gates and submission controlsThree different performance tasks
The group presentation demonstrates a business plan and applies AI or machine learning to customer needs with national-security consideration.
- The 30-minute group business-plan presentation
- AI fit and customer needs
- Implementation budget and promotion
- The individual reflective essay
- Evidence and changed judgement
- CEIE participation hurdle
It asks for a connected business argument: who experiences the problem, why the proposed use of artificial intelligence changes the outcome, how value reaches the customer, how the venture earns and spends money, and how social, legal, ethical and national-security constraints shape the design.
How the semester develops the argument
The opening lessons establish venture logic, customer value and the Lean Canvas.
The next block explains artificial intelligence at a high level and studies AI-enhanced business models across sectors. CEIE activities add founder and market perspectives.
Later lessons connect digital communication, channels and metrics to national-security concepts, costs, revenue and case analysis before groups present a business plan and each student completes reflective practice.
What makes the assessment demanding
The graded work is cumulative.
The proposal requires a concise investor-facing account, a one-page Lean Canvas with elaboration, an implementation timeline, references and a clear work allocation. The presentation must demonstrate the business plan, show how AI or machine learning meets customer needs and address national-security considerations.
The reflective essay then asks the student to analyse an AI-enhanced business case, evaluate a social-media channel used in the project and discuss challenges across social, ethical, legal and national-security dimensions.
Where marks are won
The proposal rubric rewards specificity across the canvas.
Problems must fit the target segment; the value proposition must be distinctive; the solution must answer the stated problems; revenue and cost items need explicit estimates; key metrics need a rationale; the advantage needs defensibility; and the implementation timeline must be feasible. Good writing and organisation matter, but they cannot rescue an incoherent venture model.
The strongest submission reads as one causal chain rather than a set of independent boxes.
Two gates outside the grade calculation
Course completion also depends on attendance. Students must complete at least 9 CEIE contact hours plus evaluations and attend at least 80% of instructor-led classroom sessions. Failure of either condition causes course failure regardless of assessment grades.
Track both conditions on the course Moodle page throughout the semester rather than trying to repair them after the written work is complete.
Worked example · free
Test whether an AI venture idea forms one coherent chain
- 2Name the customer job and existing alternative.
- 3State the changed outcome and a measure for the pilot.
- 5Connect revenue, cost and one defensible asset.
Key terms
- Technology Entrepreneurship
- The process of launching ventures and exploiting business opportunities created by advances in science and technology.
- Early Adopter
- A narrowly defined first customer whose urgent problem and willingness to try a new solution make learning possible.
- Lean Canvas
- A compact business-model map connecting customer problems, value, solutions, channels, economics, metrics and defensibility.
- Value Proposition
- A promise of a specific customer outcome that explains why the offer is preferable to the current alternative.
- Minimum Viable Product
- The smallest testable version that can produce evidence about the riskiest business assumption.
- Key Metric
- A measure selected to show whether the venture is creating the customer or business result it claims.
- Unfair Advantage
- A valuable asset or position that competitors cannot readily copy or purchase.
- Inference
- The use of a trained model to produce a classification, prediction or generated output for new input.
- Revenue Stream
- The mechanism through which a customer or partner pays the venture for value received.
- Cost Structure
- The fixed and variable resources required to build, deliver and support the venture.
- Technological Interdependence
- Reliance among technologies, suppliers, platforms or jurisdictions that can create shared capability and shared exposure.
- National Security
- A course lens for evaluating how technologies, dependencies, information and law affect protected public interests.
- Reflective Practice
- Structured analysis that connects experience and evidence to revised judgement and future action.
GEL2026 FAQ
How is the course assessed?
The numeric grade comes from a group commercialisation proposal worth 20%, a group oral business-plan presentation worth 30%, and an individual reflective essay worth 50%. The same course materials also set attendance-based pass conditions that sit outside the 100% numeric total, so completing the written work alone does not establish course completion.
What attendance rules can affect whether I pass?
You must complete 9 contact hours plus evaluations and attend at least 80% of classroom sessions delivered by the instructor. This is a course-fail-hurdle consequence: failure of either condition causes course failure regardless of assessment grades. Track both conditions on the course Moodle page during the semester.
What belongs in the group proposal?
The detailed brief requires an executive summary, a concise Lean Canvas with any needed elaboration, an implementation timeline, relevant references and a clear distribution of member work. It also asks for explicit revenue and cost estimates so a potential investor can judge whether the proposed venture is feasible before its starting resources are exhausted.
Why do the proposal word counts and group sizes differ across files?
The formal outline and detailed Moodle brief contain different group-size and total-length ranges. This guide preserves that conflict because merging the numbers would create a requirement that no source states. Use the current task brief and instructor direction for your class, while keeping the stable content and rubric requirements in your plan.
What should the oral presentation demonstrate?
The published outline describes a 30-minute group business-plan presentation. It should apply AI or machine learning to customer needs in a specific business sector, compare business value, estimate implementation resources, address national-security education or law considerations and explain how social-media or digital promotion supports the venture.
What does the individual reflective essay cover?
The formal outline describes an essay of about 1,200 English words with three connected tasks: analyse a real AI-enhanced business case and its sustainability, evaluate the effectiveness of a social-media channel used by the group, and discuss challenges together with social, ethical, legal and national-security implications.
Can I use generative AI in the coursework?
The course policy permits specified uses subject to instructor restrictions, critical evaluation and declaration. AI-assisted parts and the extent of assistance must be identified on the cover page, the acknowledgement must be selected at submission, and generated material normally belongs in an appendix unless the tool was used only to revise your own writing.
Which reflective-essay deadline should I follow?
The dated schedule and assessment table show 3 December 2026 at 23:59, while the live Moodle submission item is titled with 4 December 2026 and displays that later date. Because the operational surfaces conflict, check the current Moodle submission setting rather than relying on a merged or inferred deadline.
Questions students ask in each GEL2026 chapter
- How is a technology idea different from a business opportunity?
- A technology idea describes a capability. A business opportunity adds a defined customer, an important job or problem, evidence that the outcome matters, a way to deliver the result and a model for capturing enough value to continue. The capability can be impressive while the opportunity remains weak. Chapter 01 · Technology Entrepreneurship and Venture Logic
- Which Lean Canvas box should I complete first?
- Start with the early adopter and problem because every later box depends on them. The course materials provide a suggested sequence, but the deeper rule is causal: the value proposition must answer the named problem, the solution must deliver the value and the channel and economics must fit the buyer. Chapter 02 · Customer Problems and the Lean Canvas
- What is the difference between rule-based and example-based AI?
- Rule-based AI follows decision logic written by people, while example-based AI learns a model from patterns in data. The first makes rules explicit but can become brittle when situations multiply. The second can handle complex patterns, but its behaviour depends on the examples, labels, objective and operating context. Chapter 03 · How AI Learns and Creates Business Value
- What sections does the business proposal need?
- The detailed guidelines call for an executive summary, a Lean Canvas with any appropriate elaboration, an implementation timeline, relevant references and the distribution of member work. The cover and formatting requirements also matter. Use the current Moodle brief for operational details because some ranges conflict across the available files. Chapter 04 · Building the Commercialisation Proposal
- How should national security appear in a venture analysis?
- Translate the lens into the assets, capabilities, dependencies, actors and consequences of the proposed model. Ask what data or capability is sensitive, who can access or interrupt it, which external dependencies are created and who owns the response. The answers may alter architecture, partners, channels or customers. Chapter 05 · National Security, Ethics and Legal Constraints
- What should the group presentation add beyond the proposal?
- The presentation should make the business decision easy to follow. Show the customer need, how AI or machine learning changes the outcome, the business value before and after implementation, the resources required, the promotion route and the national-security or legal considerations. Use evidence and ownership rather than dense proposal text. Chapter 06 · Pitching, Reflection and Course Hurdles
How to prepare for the assessments
Build one venture idea across the semester and revise it whenever a new topic exposes a weak link. Start with the early adopter and existing alternative, then add the value proposition, solution, evidence, channel, metrics, revenue, costs and advantage. Keep a separate risk log for legal, ethical and national-security constraints.
Before each assessed submission, read the venture as one causal chain and mark every claim that lacks observable evidence, an owner or a decision rule. This produces more useful revision than repeatedly polishing the same prose.
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