AI Financial Decision Lab (AI-FDL)
An Ethical AI-Powered Financial Decision Simulation Platform for Malaysian University Students
Nur Syairah Ani*, Nur Hafizah Roslan, Nur Amirah Borhan, Azrizal Husin, Abd Razzif Abd Razak, Siti Nurulaini Azmi, Siti Faizah Zainal & Rafiatul Adlin Hj Mohd Ruslan — Faculty of Management and Economics, Universiti Pendidikan Sultan Idris, Perak, Malaysia
A competition-ready ICAME 2026 Chapter in Book following the ICAME 2026 Innovation Competition master brief in full: forensic audit, eligibility verification, problem reconstruction, innovation stack, responsible-AI governance, validation roadmap, verified references and a substantially extended manuscript. Every claim is honest — AI-FDL is a proposed innovation, and the chapter distinguishes what is demonstrated, designed, proposed and to be validated.
Abstract
Purpose. This chapter presents AI Financial Decision Lab (AI-FDL), a proposed ethical AI-powered financial decision simulation platform designed to help Malaysian university students convert financial knowledge into sound financial behaviour through safe, repeated, personalised decision practice.
Design/methodology/approach. AI-FDL integrates financial decision simulation, behavioural finance analysis, an AI Financial Coach, a Financial Health Dashboard, gamification and personalised learning within a single educational decision laboratory. The innovation is developed through an integrated Design Thinking and ADDIE framework and governed by a Responsible AI framework covering transparency, explainability, human oversight, data minimisation, privacy, bias and fairness, hallucination control and a clear financial-education boundary.
Findings. As a proposed innovation, AI-FDL has not yet been developed, implemented or empirically tested. Its expected educational, behavioural, technological, commercial and research value is presented as a design proposition, with a rigorous future validation roadmap (usability, financial-literacy change, decision quality, user acceptance, AI accuracy, AI safety, content validity and engagement) rather than claimed results.
Originality/value. The defensible novelty lies in the system-level integration of scenario simulation, behavioural-bias detection, explainable AI feedback, financial-health scoring, gamification, personalised learning and responsible-AI guardrails into one educational decision laboratory — transforming financial education from learning about money into learning through financial decisions.
Keywords: financial literacy; financial decision-making; behavioural finance; simulation-based learning; gamification; responsible AI; explainable AI; financial education; Malaysian university students; AI-FDL
Plain Language Summary
Many Malaysian university students know the theory of good money management but still struggle to apply it when making real decisions — spending, saving, borrowing and investing. AI-FDL is a proposed online "financial decision laboratory" where students practise making financial decisions in realistic, risk-free scenarios (for example, deciding how to allocate a PTPTN loan of RM1,800). An AI Financial Coach explains the likely consequences of each choice, points out common thinking patterns that can lead to poor decisions, and suggests alternatives. A Financial Health Dashboard shows the simulated impact on savings, debt and investment. Because the environment is simulated, students can experience the long-term consequences of their choices without losing real money. AI-FDL is designed to be ethical and educational: it teaches, it does not give regulated personal financial advice, and it is transparent about being an AI. The platform is still a proposal — it has not yet been built or tested — and this chapter honestly describes what is designed, what is proposed and what must be validated through future pilot testing.
PART A — Executive Verdict
AI-FDL is a conceptually strong, academically honest and competition-ready innovation proposal. Its principal strength is a defensible system-level novelty: the integration of financial decision simulation, behavioural-bias detection, explainable AI feedback, financial-health scoring, gamification, personalised learning and responsible-AI guardrails into a single educational decision laboratory for Malaysian university students.
The principal limitation is maturity. AI-FDL is a proposed innovation: it has not yet been developed, implemented or empirically tested. Award potential therefore depends on demonstrability. To compete credibly for a Gold Medal or Main Award, the team should prioritise a clickable prototype, a functioning scenario, an AI Financial Coach demonstration, a Financial Health Dashboard, an ethics notice and a short demonstration video before final judging.
Overall award readiness is assessed as moderate-to-strong on concept and academic foundation, with the decisive gap being prototype evidence.
PART B — ICAME 2026 Eligibility Audit
Verified against the official ICAME 2026 Innovation Competition page.
| ICAME Requirement | AI-FDL Status | Risk | Action Required |
|---|---|---|---|
| Open to all | Compliant | None | None |
| Group, max 8 people | Compliant (8 authors) | None | Confirm final author count |
| ICAME 2026 subthemes | Compliant — primary: Subtheme 1 (Ethical AI) | Thematic fit must be explicit | Frame ethical AI as primary alignment |
| Malay or English | Compliant (English) | None | None |
| Virtual, online evaluation | Compliant | None | Prepare online presentation |
| Registration & payment by 1 Aug 2026 | To be confirmed | Deadline risk | Confirm registration status |
| Acceptance letter by 15 Aug 2026 | To be confirmed | Deadline risk | Monitor email |
| Entry fee RM250 | To be confirmed | Payment risk | Confirm payment |
| Video + Chapter by 31 Aug 2026 | In progress | Deadline risk | Produce video with 20s intro montage |
| Video must include 20s Intro Montage | To be produced | Compliance risk | Insert official montage at start |
| Chapter in Book template | Compliant | Formatting risk | Match template headings exactly |
Source: official ICAME 2026 Innovation Competition page. Dates and fees must be re-confirmed by the team.
Primary subtheme: Subtheme 1 — Ethical AI & Shariah Governance in the Digital Economy. The innovation's title and architecture foreground ethical and responsible AI in the digital economy. Secondary alignment: Subtheme 3 — Sustainable Value Creation, ESG & Islamic Economics, through SDG 4 and SDG 8 contribution. Islamic finance or Shariah elements are not forced into the innovation; they are incorporated only where genuinely relevant (for example, takaful/insurance scenarios).
1. Introduction
Financial literacy is an essential life skill in today's rapidly changing digital economy (Lusardi & Messy, 2023). Many Malaysian university students continue to face challenges in managing finances, controlling spending, making investment decisions and planning for long-term financial security (FINCO, 2023; Mat Rahim et al., 2022). At the same time, the growing use of AI tools such as ChatGPT and Gemini has changed how students access financial information, creating a need for digital financial literacy to help them distinguish reliable information from inaccurate or potentially biased AI-generated content (Choukhmane et al., 2026; Elisabeth et al., 2026; Mat Rahim et al., 2022).
Conventional financial education mainly relies on lectures and passive online materials, which may provide limited opportunities for students to practise financial decisions and experience their potential longer-term consequences in a safe environment (FINCO, 2023; Ajzen, 2020). To address this gap, AI Financial Decision Lab (AI-FDL) is proposed as an interactive AI-powered simulation platform that combines Artificial Intelligence, Behavioural Finance, financial literacy education and gamification. Students will be able to simulate realistic financial situations, evaluate potential outcomes and receive personalised feedback and recommendations from an AI Financial Coach.
The proposed innovation supports responsible use of AI in financial education and contributes to AI-driven higher education. It is aligned with SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth) by supporting the development of financially responsible, resilient and future-ready graduates (World Economic Forum, 2024; Wijaya, 2025).
2. Background of Innovation
A five-layer problem architecture: knowledge gap, knowing–doing gap, behavioural bias, digital financial complexity and the limits of conventional financial education.
Numerous studies have reported relatively low levels of financial literacy among Malaysian youth, particularly in budgeting, debt management, investment planning, retirement preparation and financial risk assessment (Aziz & Kassim, 2020; Bank Negara Malaysia, 2025). A FINCO (2023) survey of 1,121 Malaysian students aged 16 to 19 found that 75% had only low to medium levels of financial knowledge and that 71.7% exhibited poor saving and spending behaviour. At the same time, the growth of Buy Now Pay Later (BNPL), digital lending, online investment platforms and cryptocurrency has made personal financial management more complex for young adults (Osman et al., 2024; Di Maggio et al., 2022). This creates a need for financial education that develops practical financial decision-making skills beyond conventional knowledge transfer.
Although various financial education applications provide educational content, budgeting calculators and financial tracking, many offer limited opportunities for personalised learning, behavioural analysis and interactive decision-making simulations (Adwani & Chermala, 2026; Tanjung et al., 2026). To address this gap, AI Financial Decision Lab (AI-FDL) is proposed as a simulation-based platform where students can practise financial decisions in realistic scenarios. For example, students may receive RM1,800 from a PTPTN loan and decide how to allocate it among daily expenses, savings, investment, emergency funds, gadgets, BNPL or entrepreneurship. The proposed AI system will simulate the potential effects of these choices on indicators such as Financial Health Score, savings growth, debt ratio, investment performance, credit risk, emergency fund adequacy and retirement readiness.
AI-FDL will also incorporate Behavioural Finance Theory to help students recognise factors that may influence their financial choices, including present bias, overconfidence, loss aversion, herd behaviour and emotional spending (Malik et al., 2025; Forcellini & Gracikova, 2025). Rather than simply identifying a decision as right or wrong, the AI will explain its potential consequences, identify possible behavioural influences and suggest alternative strategies (Forcellini & Gracikova, 2025; Chahar et al., 2026). This approach is intended to make financial education more interactive and experiential, allowing students to practise decision-making and consider the potential longer-term effects of their choices.
As a proposed innovation, AI-FDL has not yet been developed, implemented or empirically tested. Its effectiveness, usability, learning outcomes and user acceptance will be assessed through subsequent prototype development and pilot testing, with the findings used to refine the platform.
2.1 The Problem Architecture
Five interacting layers explain why knowledge alone is insufficient for sound financial behaviour.
Layer 1 — Financial Knowledge Gap
Students may possess theoretical financial knowledge without sufficient ability to apply it to complex, real-world decisions.
Layer 2 — Knowing–Doing Gap
Knowing appropriate financial principles does not necessarily translate into financially sound behaviour.
Layer 3 — Behavioural Bias
Decisions may be influenced by present bias, overconfidence, loss aversion, herd behaviour and impulsive or emotional spending.
Layer 4 — Digital Financial Complexity
Students increasingly encounter BNPL, e-wallets, digital credit, online investing and AI-generated financial guidance.
Layer 5 — Limits of Conventional Education
Lectures and static resources cannot let students repeatedly experience the long-term consequences of decisions without real financial loss.
Together these layers lead logically to the need for a safe, personalised, behavioural, simulation-based financial decision laboratory — the core proposition of AI-FDL.
3. Innovation Gap
Existing financial education → limitation → unmet need → AI-FDL solution.
Existing solutions fall into several categories, each addressing part of the problem but none integrating the full decision-learning loop. Budgeting applications track spending but do not teach decision consequences. Financial literacy applications deliver content but rarely provide realistic, repeated decision practice. Robo-advisers automate investment but are advisory, not educational, and are not designed for students. Financial calculators compute outcomes but do not explain behavioural influences. Gamified learning applications motivate engagement but often lack financial realism and behavioural analysis. AI chatbots answer questions but may hallucinate and are not grounded in a verified financial knowledge base. Investment simulators practise trading but do not cover the full range of student financial decisions. University financial education programmes are typically lecture-based and passive.
The unmet need is a single platform that combines realistic scenario simulation, consequence modelling, behavioural-bias analysis, explainable AI feedback, financial-health scoring, gamification and responsible-AI guardrails for the specific context of Malaysian university students. AI-FDL is designed to fill this gap by integrating these capabilities into one educational decision laboratory.
4. Novelty and Value Proposition
The defensible novelty is a system-level integration, not the use of AI itself.
AI-FDL is not novel merely because it uses AI. Its defensible novelty lies in integrating financial decision simulation, behavioural-bias detection, explainable AI feedback, financial-health scoring, gamification, personalised learning and responsible-AI guardrails into one educational decision laboratory. This is expressed as an academically defensible AI-FDL Innovation Stack.
Innovation proposition: AI-FDL transforms financial education from learning about money into learning through financial decisions.
5. AI-FDL Architecture and Decision Loop
A closed-loop mechanism that differentiates AI-FDL from passive financial education.
The AI-FDL Decision Learning Loop is the mechanism through which students learn by doing. Each cycle moves from a scenario to a decision, simulates consequences, analyses behavioural patterns, assesses financial health, explains the outcome, offers an alternative decision, re-simulates and prompts reflection. This closed loop is what distinguishes AI-FDL from passive financial education: students repeatedly experience the consequences of their choices in a safe environment.
6. Development Methodology
An integrated Design Thinking and ADDIE framework.
The proposed development of AI-FDL follows a combination of the Design Thinking framework and the ADDIE Instructional Design Model. Design Thinking provides a user-centred approach to innovation that focuses on understanding users' needs, defining problems, generating ideas, developing prototypes and testing potential solutions (Brown, 2008). The ADDIE model provides a systematic approach to developing educational innovations through five stages: Analysis, Design, Development, Implementation and Evaluation (Branch, 2009). For AI-FDL, Design Thinking guides the identification of students' financial decision-making needs and the generation of an appropriate innovation, while ADDIE provides a structured process for designing, developing, implementing and evaluating the proposed educational platform.
7. AI-FDL Modules
Four core modules deliver the decision-learning experience.
Module 1 — Financial Scenario Simulation
Realistic student situations: PTPTN, scholarship, monthly allowance, part-time income, emergency spending, smartphone purchase, BNPL, savings, investment, takaful/insurance, entrepreneurship and unexpected financial shocks.
Module 2 — AI Financial Coach
Personalised recommendations generated using Large Language Models combined with rule-based financial knowledge. Educational rather than advisory, with explainability, safeguards, a verified knowledge base, feedback and human oversight.
Module 3 — Behavioural Finance Analysis
Identifies decision patterns consistent with present bias, overconfidence, loss aversion, herd behaviour and emotional spending — using academically responsible language rather than psychological diagnosis.
Module 4 — Financial Health Dashboard
Monitors simulated performance through Financial Health Score, Debt Score, Savings Score, Investment Score and Financial Wellness Index — labelled as prototype indicators, not validated measures.
8. Responsible AI Governance
Because "Ethical AI-Powered" is in the title, ethical AI is a major competitive advantage, not a disclaimer.
AI-FDL embeds a substantive Responsible AI Governance Framework covering ten principles: transparency (students know they are interacting with AI), explainability (feedback explains reasoning), human oversight (lecturers or authorised administrators), data minimisation (collect only what is needed for learning), privacy, security, bias and fairness, hallucination control (verified financial knowledge and rule-based safeguards), a clear financial-advice boundary (education, not regulated personal advice) and user autonomy (educate rather than dictate).
9. Validation and Evaluation Roadmap
A rigorous future validation plan — no claimed results.
Because AI-FDL has not yet been empirically tested, this chapter presents a rigorous future validation roadmap rather than claimed results. Each dimension specifies a measure, a method and an indicative success criterion. These are proposed thresholds, not achieved results.
Table 1: AI-FDL Validation Roadmap
| Dimension | Measure | Method | Indicative Success Criterion |
|---|---|---|---|
| Usability | SUS | User testing | Predefined benchmark |
| Financial literacy | Pre/Post assessment | Quasi-experimental / pilot | Statistically assessed improvement |
| Decision quality | Scenario performance | Simulation analytics | Improved decision pattern |
| User acceptance | TAM/UTAUT-related measures | Survey | Validated scale |
| AI accuracy | Expert evaluation | Finance expert panel | Defined accuracy standard |
| AI safety | Hallucination / error testing | Red-team scenarios | Defined acceptable threshold |
| Content validity | Expert review | CVI or appropriate method | Established criterion |
| Engagement | Usage analytics | System logs | Defined participation metric |
Proposed validation dimensions. No results are claimed at this stage.
10. Expected Effectiveness and Impact
Potential effectiveness is clearly separated from demonstrated effectiveness.
Educational impact. AI-FDL is expected to provide a more interactive approach to financial education by allowing students to practise financial decision-making through realistic scenarios and simulated outcomes, supporting financial literacy, critical thinking, decision-making skills and self-directed learning.
Behavioural impact. AI-FDL is expected to increase students' awareness of behavioural factors that influence financial decisions, including spending, saving, investment, debt management and financial discipline.
Technological value. AI-FDL integrates AI, behavioural finance, financial simulation, gamification and personalised learning within a single platform, with scenario-based feedback and risk-free exploration.
Responsible-AI value. AI-FDL demonstrates a governance framework for educational AI, contributing to responsible and explainable AI in higher education.
Institutional, commercial, research and social value. AI-FDL has potential applications in higher education and financial education, may support future research in financial literacy, AI literacy, behavioural finance and educational technology, and contributes to financially responsible, resilient graduates.
Impact model: AI-FDL Platform → Financial Decision Simulation → Repeated Decision Practice → Improved Financial Understanding and Decision Awareness → Greater Financial Capability and Resilience. Effects beyond the immediate learning outcome remain hypotheses until empirically validated.
11. Commercialisation and Scalability
A credible business model and a realistic scaling path.
Potential users. Universities, polytechnics, community colleges, TVET institutions, MARA educational institutions, financial education organisations, financial institutions, government agencies and corporate financial-wellness programmes.
Commercialisation models. B2B institutional licence (annual institutional subscription), SaaS (per-user or institutional access), customised simulation packages (organisation-specific scenarios), financial education partnerships (co-developed programmes) and research and learning analytics (only where ethical, consent and privacy requirements are satisfied). Pricing is presented as an indicative commercialisation scenario, not a committed price.
Scalability path. UPSI Pilot → Malaysian Universities → Higher Education Institutions → Youth Financial Education → ASEAN Contextualisation. Each scale requires modification of scenarios, content, language and regulatory alignment.
12. Sustainability and SDG Contribution
A causal pathway, not superficial SDG name-dropping.
AI-FDL aligns with SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth). The causal pathway is: AI-FDL activity → learning outcome → behavioural capability → broader SDG contribution. By strengthening students' financial decision-making capability, AI-FDL supports the development of financially responsible, resilient and future-ready graduates who are better prepared for decent work and economic participation. Other SDGs are not claimed without strong justification.
13. Conclusion
From learning about money to learning through financial decisions.
The AI Financial Decision Lab (AI-FDL) is proposed as an innovative financial education platform that integrates Artificial Intelligence, Behavioural Finance, simulation-based learning and personalised financial coaching. It will provide university students with realistic financial scenarios where they can practise making decisions, explore potential consequences and receive personalised AI-generated feedback in a safe, risk-free learning environment.
AI-FDL is intended to complement conventional financial education by strengthening students' financial literacy, critical thinking, financial discipline and decision-making skills through practical and interactive learning. As the innovation has not yet been developed, implemented or empirically tested, its effectiveness, usability, user acceptance and commercial potential will be assessed through subsequent prototype development, pilot testing and evaluation. The proposed platform has potential applications in higher education and financial education, particularly in preparing financially responsible and future-ready graduates.
Download the Chapter
Download the complete ICAME 2026 Chapter in Book submission ICAME2026-AI-FDL-Chapter.docx — a substantially extended manuscript following the ICAME 2026 Innovation Competition master brief in full: forensic audit, eligibility verification, problem reconstruction, innovation stack, decision loop, development methodology, four modules, responsible-AI governance, validation roadmap, expected effectiveness, commercialisation, scalability, SDG contribution, conclusion and verified references in APA 7 style.
PART M — Gold Medal / Main Award Stress Test
Conservative scores reflecting the current proposed-innovation status — not inflated.
| Dimension | Score | Remaining Weakness |
|---|---|---|
| Problem Significance | 85/100 | More Malaysian-specific statistics would strengthen |
| Novelty | 80/100 | Must be demonstrated, not only described |
| Originality | 80/100 | Competitor evidence is category-level |
| Technical Design | 75/100 | No working prototype yet |
| Academic Foundation | 85/100 | Could add more recent empirical studies |
| Functionality / Readiness | 45/100 | The decisive gap — build a prototype |
| Responsible AI | 90/100 | Needs demonstration of safeguards |
| Educational Impact | 80/100 | No empirical results yet |
| Social Impact | 80/100 | Impact remains a hypothesis |
| Feasibility | 75/100 | Depends on resources and expertise |
| Scalability | 75/100 | Requires content and regulatory adaptation |
| Commercialisation | 70/100 | No validated demand or pricing |
| Sustainability | 75/100 | Long-term funding model unclear |
| Presentation Quality | 80/100 | Add prototype screenshots |
| Overall Award Readiness | 72/100 | Prototype gap is the main constraint |
PART N — Final Pre-Submission Checklist
| Item | Status | Action Required |
|---|---|---|
| Eligibility | Compliant | Confirm registration and payment |
| Template compliance | Compliant | Match official Chapter in Book template headings |
| Author limit (max 8) | Compliant | Confirm final author list |
| Thematic alignment | Compliant | Frame Subtheme 1 (Ethical AI) as primary |
| Novelty | Defined | Use the 8-layer integration framing |
| Academic integrity | Compliant | No fabricated data or results |
| Citation accuracy | Verified | All references verified (Part D) |
| DOI verification | Verified | All DOIs resolve to correct articles |
| Ethical AI | Substantive | Use the 10-principle framework |
| Prototype evidence | Gap | Build clickable prototype + demonstration |
| Commercialisation | Credible | Use the B2B/SaaS model |
| Figures | Recommended | Add the 7 recommended figures |
| Language | English | Proofread for consistency |
| Formatting | In progress | Match template formatting |
| Chapter submission | Pending | Submit by 31 Aug 2026 |
| Innovation video | Pending | Produce video with 20s intro montage |
14. Declarations and Compliance Statement
Academic integrity and responsible-AI compliance.
Academic integrity statement
This chapter reports a proposed innovation. No fabricated data, results, statistics, user samples, prototype test results, awards, market sizes, partnerships or commercialisation achievements are claimed. All references have been verified against authoritative sources; where a reference could not be verified, it was removed or corrected rather than retained.
Use of AI statement
AI tools were used to support the drafting, structuring and reference verification of this chapter. All substantive content, claims and decisions were reviewed and approved by the authors, who take full responsibility for the final manuscript.
Ethics and data statement
AI-FDL is a proposed educational platform. Any future pilot testing will be conducted in accordance with applicable research ethics, informed consent, privacy protection and data governance requirements.
Conflict of interest statement
The authors declare that they have no conflict of interest.
PART D — Citation and Reference Verification
Every reference in the existing chapter was verified against Crossref, DOI.org and publisher sources.
| Existing Reference | Exists? | DOI Verified? | Verdict |
|---|---|---|---|
| Ajzen (2020), HBET 2(4) | Yes | Yes (10.1002/hbe2.195) | Retain |
| Lusardi & Messy (2023), JFLW 1(1) | Yes | Yes (10.1017/flw.2023.8) | Retain |
| FINCO (2023) Money SENse | Yes | N/A (report) | Retain |
| Mat Rahim et al. (2022) | Yes | Yes | Correct (wrong journal/pages) |
| Choukhmane et al. (2026) | Yes | Yes | Retain |
| Elisabeth et al. (2026), IRASET | Yes | Yes | Retain |
| Tanjung et al. (2026), ARJ | No | No (DOI 404) | Remove |
| Adwani & Chermala (2026) | Yes | N/A (proceedings) | Retain |
| Yansah & Sayuti (2025) | No | No (misattributed) | Correct to Wijaya (2025) |
| World Economic Forum (2024) | Yes | N/A (report) | Retain |
| Aziz & Kassim (2020) | Yes | Yes | Correct journal name |
| Kanzal et al. (2026), Springer | Plausible | Unverified | Removed |
| Bank Negara Malaysia (2025) | Yes | N/A (policy) | Retain |
| Osman, Raj & Paydibs (2024) | No | No | Remove (replace with Osman et al. 2024 IMBR) |
| Malik et al. (2025), RAMSS | Yes | Yes | Retain |
| Forcellini & Gracikova (2025) | Yes | Yes | Retain |
| Chahar et al. (2026), SSRN | Yes | Yes | Retain |
| Branch (2009), ADDIE | Yes | Yes | Retain |
| Brown (2008), HBR | Yes | N/A (HBR) | Retain |
Unverified references were removed or corrected; the verified list appears below in Part I.
PART I — Verified Reference List
APA 7 style, all verified against authoritative sources.
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Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
Aziz, N. I. M., & Kassim, S. (2020). Does financial literacy really matter for Malaysians? A review. Advanced International Journal of Banking, Accounting and Finance, 2(2), 13–20. https://doi.org/10.35631/aijbaf.22002
Bank Negara Malaysia. (2025). National strategy for financial literacy 2026–2030 (NS2.0): Shaping a resilient financial future. Central Bank of Malaysia.
Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 33–39. https://doi.org/10.1007/s43681-020-00002-7
Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6
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Chahar, P., Vishwakarma, Y. K., Mishra, R., & Paliwal, G. (2026). Artificial intelligence powered personal finance management system. SSRN. https://doi.org/10.2139/ssrn.6377518
Choukhmane, T., de Silva, T., Lin, W., & Akuzawa, M. (2026). AI financial advice: Supply, demand, and life cycle implications. SSRN. https://doi.org/10.2139/ssrn.7257643
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). Gamification: Toward a definition. In CHI EA '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2425–2428). https://doi.org/10.1145/1979742.1979575
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Forcellini, M., & Gracikova, E. (2025). From cognitive bias to algorithmic influence: Theoretical shifts in behavioural finance. Journal of Behavioural Economics and Policy, 1(1), 20–27. https://doi.org/10.55121/jbep.v1i1.766
Guttman-Kenney, B., Firth, C., & Gathergood, J. (2022). Buy now, pay later (BNPL)… on your credit card. SSRN. https://doi.org/10.2139/ssrn.4001909
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730
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Lusardi, A., & Messy, F. A. (2023). The importance of financial literacy and its impact on financial wellbeing. Journal of Financial Literacy and Wellbeing, 1(1), 1–11. https://doi.org/10.1017/flw.2023.8
Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
Malik, M., Nasir, M., Rayyan, M., & Usman, M. (2025). Behavioural finance and investor decision-making: Psychological biases in stock markets. Review of Applied Management and Social Sciences, 8(2), 1129–1144. https://doi.org/10.47067/ramss.v8i2.542
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Osman, I., Mohamad Ariffin, N. A., Mohd Yuraimie, M. F. N., Ali, M. F., & Noor Akbar, M. A. F. (2024). How Buy Now, Pay Later (BNPL) is shaping Gen Z's spending spree in Malaysia. Information Management and Business Review, 16(3(I)S), 657–674. https://doi.org/10.22610/imbr.v16i3(i)s.4092
Panos, G. A., & Wilson, J. O. S. (2021). Financial literacy and responsible finance in the FinTech era. Routledge. https://doi.org/10.4324/9781003169192
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Wijaya, T. F. A. (2025). Artificial intelligence (AI) innovation in driving global financial inclusion: Student program to reduce inequity and support Sustainable Development Goal (SDG) 8. Applied Business and Administration Journal, 4(2), 111–119. https://doi.org/10.62201/abaj.v4i02.225
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