ACCT 5111W

 

Class sessions (Saturdays): Sep 12, 19; Oct 10, 17, 24, 31; Nov 7, 14, 21, 2:00pm–5:30pm

 

Final exam: Saturday Nov 28, 2:00pm–5:30pm

 

Refer to the Teamup calendar for the most up-to-date classroom locations.

 

1.       COURSE DESCRIPTION

This course examines corporate financial reporting as a business language and decision-making system. Students will learn how the income statement, balance sheet, and statement of cash flows are connected; how managers’ incentives affect reporting choices; and how users interpret accounting information for planning, control, valuation, governance, and ethical judgement. The course encourages responsible AI use for formative learning while requiring students to demonstrate personal competence without AI assistance in weekly in-class quizzes and the final examination.

 

Understanding and analysing actual annual reports and corporate disclosures constitute an integral part of this course. Students are expected to use AI during preparation, but not as a substitute for professional judgement. In class, we will scrutinise financial statements and connect accounting information to real business decisions and ethical issues such as earnings manipulation and fraudulent reporting.

 

2.       LEARNING OUTCOMES

After completing this course, students should be able to:

 

1)      analyse financial statements and explain the links among the major financial statements;

2)      evaluate how accounting information supports management planning, decision-making, control, and governance;

3)      identify ethical issues, incentives, and judgment calls in financial reporting;

4)      use AI tools critically for formative learning by testing prompts, evaluating outputs, identifying errors, correcting weaknesses, and checking authoritative sources; and

5)      demonstrate independent competence in core accounting concepts, calculations, and judgments without AI assistance in weekly quizzes and the final examination.

 

3.       COURSE MATERIALS

Textbook:

The required textbook for the course is:

                     “Financial Accounting: International Financial Reporting Standards” 12th Ed. (Pearson), by Harrison, Horngren, Thomas, Tietz, and Suwardy (Note: We will not use Pearson’s MyLab feature for this book.)

 

 

Cases:

There will be three case studies. For each case, groups will prepare a PowerPoint presentation that identifies the key issues, develops a defensible recommendation or interpretation, and raises broader questions about the financial reporting environment. AI tools should be used for preparation, but students must verify outputs, exercise independent judgement, and be able to explain any submitted work. Each student must also submit an individual AI learning log after the class discussion, showing how they used, checked, corrected, and learned from AI output. Marks will reward critique, judgement, correction, and transfer of learning rather than polished AI-generated text.

 

Lecture Notes and Slides:

Lecture notes and other materials used in class will be placed on BlackBoard. No hardcopy printouts will be provided.

 

The study plan and class schedule are detailed in the Appendix.

 

4.       LEARNING ACTIVITIES

This course follows a flipped classroom approach. Students prepare before class, apply and defend accounting judgement during class, and consolidate learning afterwards.

 

Before class, students should complete assigned readings, attempt suggested exercises and identify areas of confusion. The course AI Tutor Agent, grounded in course materials and selected authoritative websites, may be used to review concepts, test explanations, check reasoning, and prepare better questions. Students may also use other AI tools, but all AI output must be checked against trusted sources and students’ own accounting reasoning.

 

During class, we will not simply repeat material that students will have studied in advance. Instead, class time will focus on applying accounting concepts to problems, cases, financial statements, reporting choices, incentives, ethical issues, and business decisions. Students should be ready to explain ideas, challenge assumptions, detect errors, defend judgements, and participate actively in discussion.

 

From session 2 onwards, weekly in-class quizzes will normally be administered at the beginning of class. They test basic concepts, simple calculations, and accounting judgement from the previous session and must be completed individually without AI assistance or communication with others. Their purpose is to help students monitor learning and prepare for the AI-free final examination.

 

Case studies connect technical accounting topics with real business settings. Groups should use AI use for preparation, but they must verify outputs, exercise independent judgement, and be able to explain their analyses. Individual AI learning logs will show how students questioned, checked, corrected, and learned from AI output. Selected groups will present their PowerPoint submissions in class and respond to questions without reading from pre-prepared notes.

 

After class, students should review mistakes, consolidate notes, complete additional practice exercises, and prepare for the next topic. The overall goal is not to produce polished notes or AI-assisted answers, but to develop durable understanding, professional scepticism, and the ability to apply accounting knowledge independently.

 

5.       EVALUATION OF STUDENTS’ PERFORMANCE

Assessment summary

Grading will be based on the following scheme:

Assessment component

Format

Weight

Individual formative assessments: weekly in-class quizzes and AI learning logs

In-class quizzes completed individually without AI; AI learning logs submitted online as instructed

15%

Group assignments: three PowerPoint case submissions and class presentations

PowerPoint files submitted online to Blackboard, as instructed

30%

Class contribution

Assessed throughout the term, including discussion, questioning, explanation, and oral defence where applicable

5%

Final examination

Individual, in class, without AI assistance or internet access

50%

Total

100%

 

Assessment details

(a) Individual formative assessments: weekly in-class quizzes and AI learning logs

This component has two parts. Weekly in-class quizzes, beginning from session 2, test students’ understanding of concepts, basic calculations, and accounting judgement from the previous session. Although AI should be used for preparation, each quiz must be completed individually, in class, without AI assistance or communication with others.

 

Case AI learning logs are submitted individually after each graded case discussion. Each log should normally be no longer than one A4 page and should demonstrate how the student used AI to support learning, evaluated the AI output, corrected weaknesses, and identified final learning points. Marks reward critical evaluation, correction, and judgement.

 

(b) Group assignments: three case studies, PowerPoint submissions

There will be three group case assignments. For each case, groups submit a PowerPoint file that identifies the key issues, relevant financial reporting choices, a defensible interpretation or recommendation, and questions for class discussion. AI use is required for preparation, but groups remain responsible for accuracy, judgement, and appropriate acknowledgement of sources.

 

Selected groups will present their case analysis and lead a Q&A session for up to 30 minutes. Submissions should clearly indicate contributors. Students must understand any material submitted with their name on it and be ready to explain and defend the reasoning behind it.

 

(c) Class contribution

Class contribution is assessed on the quality of participation over the whole term. Students are expected to prepare for discussion, respond constructively when called on, ask useful questions, and help create a serious learning environment. Credit will not normally be given for a missed or late-arrival session unless there is an accepted reason. Failure to follow instructions for assignments, quizzes, or examinations may result in grade penalties.

 

(d) Final examination

The final examination is a summative assessment covering the whole course. It must be completed individually, without AI assistance or communication with others. Only materials explicitly permitted by the instructor may be used.

 

 

Overall course grades will reflect students’ achievement of the course learning outcomes as follows:

 

Grade

Overall performance level

A

Outstanding performance on all learning outcomes.

A-

Generally outstanding performance on all, or almost all, learning outcomes.

B+/B/B-

Substantial performance on all learning outcomes, or strong performance in some areas that compensates for weaker performance in others.

C+/C/C-

Satisfactory performance on the majority of learning outcomes, with some weaknesses.

D+/D

Barely satisfactory performance on several learning outcomes.

F

Unsatisfactory performance on several learning outcomes, or failure to meet specified assessment requirements.

 

Exam, accommodation, and re-grading policies

Make-up or rescheduled exams are not available unless pre-agreed with the MBA Office or justified by documented extenuating circumstances. Job interviews, weddings, and similar personal events are not normally considered extenuating. Serious illness, contagion concerns, or family emergencies may be considered if properly documented.

 

Requests for re-grading must be submitted in writing within two weeks of receiving the grade. The request should clearly explain the alleged error or overlooked aspect. Re-grading may apply to the whole assessment item, not only the challenged part, at the instructor’s discretion. Course grade appeals may be referred to the course Assessment Panel for final review.

 

To preserve fairness, all students are bound by the same evaluation scheme. There will be no extra credit work or additional opportunities to improve grades outside the stated assessments.

 

6.       AI USE

This course treats AI as a learning partner, not a substitute for learning. Students are encouraged to use AI for formative preparation, explanation, practice, feedback, and idea testing, provided they remain critical, transparent, and responsible.

 

The instructor has prepared an AI Tutor Agent in Microsoft Copilot, grounded in the course textbook, the publisher’s solution manual, and selected authoritative accounting websites. Students may use this agent or suitable alternatives, including self-developed source-constrained agents, to review concepts, test reasoning, identify gaps, and prepare for class discussion. Regardless of the tool used, students remain responsible for checking accuracy and exercising independent judgement.

 

AI use in this course can be summarised as follows:

Learning phase

AI use

Main purpose

Pre-class learning and practice

Allowed and actively encouraged

Explanation, feedback, self-testing, and preparation

In-class discussion

Allowed only when explicitly permitted

Support learning activities directed by the instructor

AI learning logs

Required where assigned

Show questioning, checking, correction, and learning

Weekly in-class quizzes

Not allowed

Demonstrate individual understanding

Final examination

Not allowed

Demonstrate independent competence

 

 

Students should approach all AI output with healthy scepticism. A source-constrained AI agent may still produce errors, unsupported assumptions, fabricated references, biased reasoning, or fluent but misleading explanations. Students should check AI output against the textbook, official accounting sources, case facts, and their own reasoning. Formative assessments reward critique, correction, judgement, and transfer of learning; not polished AI-assisted writing.

 

Students must acknowledge meaningful AI assistance in submitted work when required. This includes identifying the tool used and briefly explaining how it supported the work, such as planning, idea generation, explanation, comparison, checking, or drafting. Where students quote, paraphrase, or include AI-generated text, they must follow the citation or acknowledgement format specified by the instructor.

 

Misrepresenting AI-generated work as one’s own original effort is academic misconduct. Responsible AI use can strengthen learning, but students must be able to explain and defend the reasoning behind any work submitted in their name.

 

7.       COURSE CONDUCT AND ACADEMIC INTEGRITY

Students are expected to behave professionally and contribute to a serious learning environment. This includes attending regularly, arriving on time, preparing for class, participating constructively, respecting others’ learning, and following instructions for class activities, assignments, quizzes, and examinations.

 

Academic integrity is essential. Students must understand and be able to explain any work submitted with their name on it, whether completed individually or as part of a group. Responsible use of approved AI tools is encouraged for formative learning and case preparation, but students remain responsible for the accuracy, judgement, and originality of their submitted work.

 

Students must not use materials from previous offerings of this course, including prior solutions, slides, reports, or files from former students or other sections. Students must also not collaborate on examinations, quizzes, or other individual assessments unless the instructor explicitly permits it. Answers for individual work must reflect the student’s own effort and understanding.

 

All sources must be properly acknowledged. In written work, students must provide complete citations, use quotation marks for any non-trivial wording taken directly from a source, and acknowledge any graphs, tables, AI-generated content, or other materials used, according to the instructor’s instructions.

 

CUHK has zero tolerance for cheating and plagiarism. Suspected cases will be reported to the Faculty Disciplinary Committee under University guidelines. For full details, students should refer to “Honesty in Academic Work: A Guide for Students and Teachers”:

http://www.cuhk.edu.hk/policy/academichonesty/index.htm   

 

Electronic submission of group assignments via VeriGuide

All computer-generated, principally text-based assignments must be submitted through VeriGuide. For this course, this includes case-related submissions and individual AI learning logs. Students should not submit documents that are not primarily their own original judgement and analysis, such as completed forms, company financial reports, or unedited AI-generated material. Failure to follow these instructions may result in grade penalties.

 

8.       DYNAMIC NATURE OF SYLLABUS

This course outline reflects the current plan for the term. The instructor may adjust topics, readings, activities, deadlines, or assessment arrangements if doing so would better support student learning or respond to practical circumstances. Any changes will be announced through Blackboard.

APPENDIX: STUDY PLAN AND CLASS SCHEDULE (subject to change)

 

Week

Date

Topics

Textbook readings; exercises and problems

Graded assignments

submission due dates

1

12-Sep-26

Uses of accounting information; financial statements overview; balance sheet components

Chapter 1 (35 to 61);

Quiz (74 to 75)

2

19-Sep-26

Recording transactions using the accounting equation; structure of income statement; accrual concept

Chapter 2 (92 to 120); Chapter 3 (162 to 172);

E2-16A; S3-14; E3-17A

Group: Case 1 - Biovail Corporation

(deadline: 24/09/2026)

3

10-Oct-26

Receivables; channel stuffing

Chapter 5 (318 to 332);

E5-26A; E5-28A; E5-50

Individual: Case 1 AI Learning Log

(deadline: 27/09/2026)

4

17-Oct-26

Statement of cash flows; basic financial ratios

Chapter 11 (656 to 686);

S11-6; S11-7; E11-20A

5

24-Oct-26

Inventory and cost of sales

Chapter 6 (366 to 386);

E6-20A; E6-40B; P6-65A

Group: Case 2 - Ceres Gardening Company

(deadline: 29/10/2026)

6

31-Oct-26

Property, plant & equipment; depreciation; intangible assets

Chapter 7 (424 to 458);

E7-17A; E7-18A; E7-21A;

E7-22A

Individual: Case 2 AI Learning Log

(deadline: 01/11/2026)

7

7-Nov-26

Liabilities and Debt; Bonds and notes

Chapter 9 (531 to 557);

E9-27A; E9-28A; E9-38B

8

14-Nov-26

Financing: equity

Chapter 10 (594 to 620);

E10-21A; E10-22A; E10-27A

Group: Case 3 – Starbucks

(deadline: 19/11/2026)

9

21-Nov-26

Investments: debt and equity securities; financial statement analysis

Chapter 8 (484 to 499); Chapter 12 (737 to 753);

E8-13A; E8-14A; E8-16A;

E8-17A; Quiz (774 to 776)

Individual: Case 3 AI Learning Log

Individual: Peer evaluations covering group assignments

(deadline: 22/11/2026)

10

28-Nov-26

FINAL EXAMINATION (No AI, no access to internet, 2 double-sided A4 sheets of notes allowed)

 

 

Notes:

Exercises and problems refer to the textbook numbering convention, for example:

E2-16A is an exercise at the end of chapter 2, group A (you can find it on pages 129-130),

S3-14 is a short exercise at the end of chapter 3 (page 204), etc.