AI 财务 决策 Lab (AI-FDL)
An Ethical AI 驱动 财务 决策 模拟 平台 for Malaysian University 学生
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 管理 and Economics, Universiti Pendidikan Sultan Idris, 霹雳, 马来西亚
A competition-ready ICAME 2026 Chapter in Book following the ICAME 2026 创新 竞争 master brief in full: forensic audit, eligibility verification, problem reconstruction, innovation stack, responsible-AI governance, validation roadmap, 已验证 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
用途. This chapter presents AI 财务 决策 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.
设计/methodology/approach. AI-FDL integrates financial decision simulation, behavioural 财务 analysis, an AI 财务 教练, a 财务 健康 仪表盘, gamification and personalised learning within a single educational decision laboratory. The innovation is developed through an integrated 设计 Thinking and ADDIE framework and 受治理的 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 设计 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 系统-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.
关键词: financial literacy; financial decision-making; behavioural 财务; simulation-based learning; gamification; responsible AI; explainable AI; financial education; Malaysian university students; AI-FDL
Plain 语言 摘要
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 财务 教练 explains the likely consequences of each choice, points out common thinking patterns that can lead to poor decisions, and suggests alternatives. A 财务 健康 仪表盘 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 — 管理层 判定
AI-FDL is a conceptually strong, academically honest and competition-ready innovation proposal. Its principal strength is a defensible 系统-level novelty: the integration of 金融决策模拟、行为偏差检测、可解释AI反馈、财务健康评分、游戏化、个性化学习以及负责任的AI护栏 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 黄金 Medal or Main Award, the team should prioritise a clickable prototype, a functioning scenario, an AI 财务 教练 demonstration, a 财务 健康 仪表盘, an ethics notice and a short demonstration video before final judging.
总体 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 审计
已验证 against the official ICAME 2026 创新 竞争 page.
| ICAME 要求 | AI-FDL 状态 | 风险 | 操作 必需 |
|---|---|---|---|
| 打开 to all | 合规 | 无 | 无 |
| 分组, max 8 people | 合规 (8 authors) | 无 | 确认 final author count |
| ICAME 2026 subthemes | 合规 — primary: Subtheme 1 (Ethical AI) | 主题契合度必须明确 | Frame ethical AI as primary alignment |
| 马来语 or 英语 | 合规 (英语) | 无 | 无 |
| 虚拟, online evaluation | 合规 | 无 | 准备 online presentation |
| Registration & payment by 1 Aug 2026 | To be confirmed | Deadline risk | 确认 registration 状态 |
| 录取通知书截止 2026 年 8 月 15 日 | To be confirmed | Deadline risk | 监控 email |
| 分录 fee RM250 | To be confirmed | Payment risk | 确认 payment |
| Video + Chapter by 31 Aug 2026 | In progress | Deadline risk | 制作含20秒片头蒙太奇的视频 |
| 视频必须包含20秒片头蒙太奇 | To be produced | 合规 risk | 在开头插入官方蒙太奇 |
| Chapter in Book template | 合规 | Formatting risk | 严格匹配模板标题 |
来源: official ICAME 2026 创新 竞争 page. Dates and fees must be re-confirmed by the team.
Primary subtheme: Subtheme 1 — Ethical AI & Shariah 治理 in the 数字化 Economy. The innovation's title and architecture foreground ethical and responsible AI in the digital economy. Secondary alignment: Subtheme 3 — 可持续 价值 Creation, ESG & Islamic Economics, through SDG 4 and SDG 8 contribution. Islamic 财务 or Shariah elements are not forced into the innovation; they are incorporated only where genuinely relevant (for example, takaful/insurance scenarios).
1. 简介
财务 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 工具 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).
传统 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 财务 决策 Lab (AI-FDL) is proposed as an interactive AI-powered simulation platform that combines 人工智能, Behavioural 财务, financial literacy education and gamification. 学生 will be able to simulate realistic financial situations, evaluate potential outcomes and receive personalised feedback and recommendations from an AI 财务 教练.
The proposed innovation supports responsible use of AI in financial education and contributes to AI-driven higher education. It is aligned with SDG 4 (质量 教育) and SDG 8 (Decent Work and Economic 增长) by supporting the development of financially responsible, resilient and future-ready graduates (World Economic Forum, 2024; Wijaya, 2025).
2. 背景 of 创新
五层问题架构：知识缺口、知行差距、行为偏差、数字金融的复杂性以及传统金融教育自身的局限。
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; 银行 Negara 马来西亚, 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 财务 决策 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 系统 will simulate the potential effects of these choices on indicators such as 财务 健康评分, savings growth, debt ratio, investment performance, credit risk, emergency fund adequacy and retirement readiness.
AI-FDL will also incorporate Behavioural 财务 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.
作为一项拟议的创新，AI-FDL 尚未被开发、实施或经过实证检验。其有效性、可用性、学习成果和用户接受度将通过后续的原型开发和小规模试点测试进行评估，并以此完善该平台。
2.1 问题 架构
五个相互作用的层次说明了为什么仅有知识不足以形成良好的财务行为。
Layer 1 — 财务 知识 Gap
学生 may possess theoretical financial knowledge without sufficient ability to apply it to complex, real-world decisions.
第 2 层——知行差距
了解正确的金融原则，并不必然转化为健全的金融行为。
Layer 3 — Behavioural 偏置
决策可能受到现时偏好、过度自信、损失厌恶、从众行为以及冲动性或情绪化消费的影响。
Layer 4 — 数字化 财务 复杂度
学生 increasingly encounter BNPL, e-wallets, digital credit, online investing and AI-generated financial guidance.
Layer 5 — 限制 of 传统 教育
课堂讲授和静态资源无法让学生在无须承担真实财务损失的情况下反复体验决策所带来的长期后果。
综合来看，这些层面在逻辑上共同指向对这样一个平台的需求： 安全、个性化、基于行为模拟的金融决策实验室 ——AI-FDL 的核心主张。
3. 创新 Gap
现有金融教育 → 局限性 → 未被满足的需求 → AI-FDL 解决方案。
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. 财务 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. 财务 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 已验证 financial knowledge base. 投资 simulators practise trading but do not cover the full range of student financial decisions. University financial education programmes are typically lecture-based and passive.
未被满足的需求是一个整合了以下能力的统一平台： 逼真的情境模拟、后果建模、行为偏差分析、可解释AI反馈、财务健康评分、游戏化元素以及负责任的AI护栏 这些能力需适配马来西亚大学生的具体情境。AI-FDL 的设计目标正是将这些能力整合为一个教育决策实验室，填补上述空白。
4. Novelty and 价值 Proposition
The defensible novelty is a 系统-level integration, not the use of AI itself.
AI-FDL 的创新之处并不在于它使用了AI。其站得住脚的创新在于整合了以下元素： 金融决策模拟、行为偏差检测、可解释AI反馈、财务健康评分、游戏化、个性化学习以及负责任的AI护栏 into one educational decision laboratory. This is expressed as an academically defensible AI-FDL 创新 技术栈.
创新 proposition: AI-FDL 将金融教育从“学习关于金钱的知识”转变为“通过金融决策来学习”。
5. AI-FDL 架构 and 决策 循环
一种将 AI-FDL 与被动式金融教育区分开来的闭环机制。
The AI-FDL 决策 Learning 循环 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 设计 Thinking and ADDIE framework.
The proposed development of AI-FDL follows a combination of the 设计 Thinking framework and the ADDIE Instructional 设计 模型. 设计 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: 分析, 设计, Development, 实施 and Evaluation (分店, 2009). For AI-FDL, 设计 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 模块
四个核心模块共同带来决策学习的体验。
Module 1 — 财务 场景 模拟
贴近真实的学生情境：PTPTN、奖学金、每月生活费、兼职收入、应急支出、购买智能手机、BNPL、储蓄、投资、回教保险/保险、创业以及意外的财务冲击。
Module 2 — AI 财务 教练
Personalised recommendations generated using 大 语言 Models combined with rule-based financial knowledge. Educational rather than advisory, with explainability, safeguards, a 已验证 knowledge base, feedback and human oversight.
Module 3 — Behavioural 财务 分析
识别与现时偏好、过度自信、损失厌恶、从众行为和情绪化消费相符的决策模式——采用学术上审慎的语言，而非心理诊断。
Module 4 — 财务 健康 仪表盘
Monitors simulated performance through 财务 健康评分, Debt 分数, 储蓄 分数, 投资 分数 and 财务 Wellness Index — labelled as prototype indicators, not validated measures.
8. Responsible AI 治理
Because "Ethical AI 驱动" is in the title, ethical AI is a major competitive advantage, not a disclaimer.
AI-FDL embeds a substantive Responsible AI 治理 框架 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 是必需的 for learning), privacy, security, bias and fairness, hallucination control (已验证 financial knowledge and rule-based safeguards), a clear financial-advice boundary (education, not regulated personal advice) and user autonomy (educate rather than dictate).
9. 验证 and Evaluation 路线图
严谨的未来验证计划——不宣称任何结果。
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 测量, a method and an indicative success criterion. These are proposed thresholds, not achieved results.
Table 1: AI-FDL 验证 路线图
| 维度 | 测量 | 方法 | 示例成功标准 |
|---|---|---|---|
| Usability | SUS | 用户 testing | Predefined benchmark |
| 财务 literacy | 前测 / 后测评估 | Quasi-experimental / pilot | Statistically assessed improvement |
| 决策 quality | 场景 performance | 模拟 analytics | 已改进 decision pattern |
| 用户 acceptance | TAM/UTAUT-related measures | Survey | 已验证 scale |
| AI accuracy | Expert evaluation | 财务 expert panel | 明确的准确度标准 |
| AI safety | 幻觉 / error testing | 红队测试场景 | Defined acceptable 阈值 |
| 内容 validity | Expert review | CVI or appropriate method | Established criterion |
| 互动 | 使用分析 | 系统 logs | 明确的参与度指标 |
拟议的验证维度。现阶段不宣称任何结果。
10. Expected Effectiveness and 影响
潜在 effectiveness is clearly separated from demonstrated effectiveness.
Educational impact. AI-FDL 预期能提供更具互动性的金融教育方式，让学生通过逼真的情境和模拟结果来练习金融决策，从而提升金融素养、批判性思维、决策能力以及自主学习能力。
Behavioural impact. AI-FDL 预期能提高学生对影响金融决策的行为因素的认识，包括消费、储蓄、投资、债务管理以及财务纪律。
Technological value. AI-FDL integrates AI, behavioural 财务, financial simulation, gamification and personalised learning within a single platform, with scenario-based feedback and risk-free exploration.
Responsible-AI value. AI-FDL 展示了一个面向教育AI的治理框架，为高等教育中的负责任且可解释的AI作出贡献。
机构, 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 财务 and educational technology, and contributes to financially responsible, resilient graduates.
影响 model: AI-FDL 平台 → 财务 决策 模拟 → Repeated 决策 业务 → 已改进 财务 Understanding and 决策 Awareness → Greater 财务 Capability and Resilience. Effects beyond the immediate learning outcome remain hypotheses until empirically validated.
11. Commercialisation and 可扩展性
可信的商业模式与切实可行的规模化路径。
潜在 users. 大学, 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). 定价 is presented as an indicative commercialisation scenario, not a committed price.
可扩展性 path. UPSI 试点 → Malaysian 大学 → Higher 教育 Institutions → Youth 财务 教育 → ASEAN Contextualisation. Each scale requires modification of scenarios, content, language and regulatory alignment.
12. 可持续发展 and SDG Contribution
一条因果路径，而非表面化的SDG引用。
AI-FDL aligns with SDG 4 (质量 教育) and SDG 8 (Decent Work and Economic 增长). 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. 其他 SDGs are not claimed without strong justification.
13. Conclusion
从学习关于金钱的知识，到通过金融决策来学习。
The AI 财务 决策 Lab (AI-FDL) is proposed as an innovative financial education platform that integrates 人工智能, Behavioural 财务, 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 旨在通过实践性和互动式学习，强化学生的金融素养、批判性思维、财务纪律和决策能力，从而补充传统的金融教育。由于该创新尚未被开发、实施或经过实证检验，其有效性、可用性、用户接受度及商业潜力将通过后续的原型开发、试点测试和评估来评定。该拟议平台在高等教育和金融教育领域具有潜在应用，尤其是在培养具备财务责任感、面向未来的毕业生方面。
下载 the Chapter
下载 the complete ICAME 2026 Chapter in Book submission ICAME2026-AI-FDL-Chapter.docx — a substantially extended manuscript following the ICAME 2026 创新 竞争 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 已验证 references in APA 7 style.
PART M — 黄金 Medal / Main Award Stress 测试
Conservative scores reflecting the current proposed-innovation 状态 — not inflated.
| 维度 | 分数 | Remaining Weakness |
|---|---|---|
| 问题 Significance | 85/100 | 更多马来西亚本土统计数据将有助于加强 |
| Novelty | 80/100 | 必须予以实证，而不仅仅是文字描述 |
| Originality | 80/100 | Competitor evidence is category-level |
| 技术 设计 | 75/100 | 目前尚无可运行原型 |
| Academic 基础 | 85/100 | 可以补充更多较新的实证研究 |
| Functionality / Readiness | 45/100 | 决定性的缺口——构建原型 |
| Responsible AI | 90/100 | 需要展示安全保障措施 |
| Educational 影响 | 80/100 | 目前尚无实证结果 |
| 社会 影响 | 80/100 | 影响 remains a hypothesis |
| Feasibility | 75/100 | 取决于资源与专业能力 |
| 可扩展性 | 75/100 | 需要内容与法规层面的本地化调整 |
| Commercialisation | 70/100 | 需求与定价尚未验证 |
| 可持续发展 | 75/100 | 长期资金模式尚不明确 |
| Presentation 质量 | 80/100 | 增加原型截图 |
| 总体 Award Readiness | 72/100 | Prototype gap is the main constraint |
第N部分——提交前最终检查清单
| Item | 状态 | 操作 必需 |
|---|---|---|
| Eligibility | 合规 | 确认 registration and payment |
| Template compliance | 合规 | 与《书内章节》官方模板的标题保持一致 |
| Author 限制 (max 8) | 合规 | 确认 final author list |
| Thematic alignment | 合规 | 将子主题 1（伦理 AI）作为主要方向 |
| Novelty | Defined | 采用八层整合框架 |
| Academic integrity | 合规 | 不得编造数据或结果 |
| Citation accuracy | 已验证 | All references 已验证 (Part D) |
| DOI verification | 已验证 | 所有DOI均能正确解析至对应文章 |
| Ethical AI | Substantive | 使用十项原则框架 |
| Prototype evidence | Gap | 构建 clickable prototype + demonstration |
| Commercialisation | Credible | 使用 B2B/SaaS 模型 |
| 图表 | 推荐 | Add the 7 recommended figures |
| 语言 | 英语 | 校对以保持一致性 |
| Formatting | In progress | 匹配模板格式 |
| Chapter submission | 待处理 | 提交 by 31 Aug 2026 |
| 创新 video | 待处理 | 制作含20秒片头蒙太奇的视频 |
14. 声明 and 合规 Statement
学术诚信与负责任AI合规。
学术诚信声明
This chapter reports a proposed innovation. No fabricated data, results, statistics, user samples, prototype test results, 奖项, market sizes, partnerships or commercialisation achievements are claimed. All references have been 已验证 against authoritative sources; where a reference could not be 已验证, it was removed or corrected rather than retained.
Use of AI statement
AI 工具 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.
伦理与数据声明
AI-FDL 是一个拟议的教育平台。未来任何试点测试都将遵循适用的研究伦理、知情同意、隐私保护以及数据治理要求。
冲突 of interest statement
作者声明不存在利益冲突。
PART D — Citation and Reference 验证
Every reference in the existing chapter was 已验证 against Crossref, DOI.org and publisher sources.
| Existing Reference | Exists? | DOI 已验证? | 判定 |
|---|---|---|---|
| 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 | 正确 (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) | 正确 to Wijaya (2025) |
| World Economic Forum (2024) | Yes | N/A (report) | Retain |
| Aziz & Kassim (2020) | Yes | Yes | 正确 journal name |
| Kanzal et al. (2026), Springer | Plausible | Unverified | Removed |
| 银行 Negara 马来西亚 (2025) | Yes | N/A (policy) | Retain |
| Osman, Raj & Paydibs (2024) | No | No | 删除（替换为 Osman 等人，2024，IMBR） |
| Malik et al. (2025), RAMSS | Yes | Yes | Retain |
| Forcellini & Gracikova (2025) | Yes | Yes | Retain |
| Chahar et al. (2026), SSRN | Yes | Yes | Retain |
| 分店 (2009), ADDIE | Yes | Yes | Retain |
| Brown (2008), HBR | Yes | N/A (HBR) | Retain |
Unverified references were removed or corrected; the 已验证 list appears below in Part I.
PART I — 已验证 Reference 列表
APA 7 style, all 已验证 against authoritative sources.
Adwani, H., 和 Chermala, A.（2026）。AI驱动的游戏化学习促进金融素养：一项关于Z世代参与度的研究。收录于 ECOFIN SUMMIT'26 Proceedings (p. 84). 国际 School of 商业 & Media. ISBN 978-93-5717-775-7.
Ajzen, I.（2020）。计划行为理论：常见问题解答。 人类 Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
Aziz, N. I. M., 和 Kassim, S.（2020）。金融素养对马来西亚人真的重要吗？一篇综述。 Advanced 国际 日志 of Banking, 会计 and 财务, 2(2), 13–20. https://doi.org/10.35631/aijbaf.22002
银行 Negara 马来西亚. (2025). 2026–2030年国家金融素养战略（NS2.0）：塑造更具韧性的金融未来. Central 银行 of 马来西亚.
Borenstein, J., 和 Howard, A.（2021）。AI的新兴挑战与AI伦理教育的必要性。 AI and Ethics, 1(1), 33–39. https://doi.org/10.1007/s43681-020-00002-7
分店, R. M. (2009). Instructional 设计: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6
Brown, T. (2008). 设计 thinking. Harvard 商业 审核, 86(6), 84–92.
Chahar, P., Vishwakarma, Y. K., Mishra, R., & Paliwal, G. (2026). Artificial intelligence powered personal 财务 management 系统. SSRN. https://doi.org/10.2139/ssrn.6377518
Choukhmane, T., de Silva, T., Lin, W., & Akuzawa, M. (2026). AI financial advice: 供应, demand, and life cycle implications. SSRN. https://doi.org/10.2139/ssrn.7257643
Deterding, S., Dixon, D., Khaled, R., 和 Nacke, L.（2011）。游戏化：迈向一个定义。收录于 CHI EA '11: Proceedings of the SIGCHI Conference on 人类 Factors in Computing 系统 (pp. 2425–2428). https://doi.org/10.1145/1979742.1979575
Di Maggio, M., Williams, E., & Katz, J. (2022). Buy now, pay later credit: 用户 characteristics and effects on spending patterns （NBER 工作论文第30508号）。https://doi.org/10.3386/w30508
Elisabeth, N., Lie, V., 和 Herlina, M. G.（2026年5月）。人工智能驱动：探讨其对印尼高等教育学生金融素养与成就需求的影响。收录于 2026 6th 国际 Conference on Innovative 研究 in 已施用 科学, 工程 and 科技 (IRASET) (pp. 1–6). IEEE. https://doi.org/10.1109/IRASET68627.2026.11538502
财务 行业 Collective Outreach [FINCO]. (2023). Money SENse：马来西亚学生对财务事务的掌握程度. FINCO 马来西亚. https://www.finco.my/wp-content/uploads/2023/07/从-Classroom-to-Careers_-学生-Transition-from-表单-5_FINCOs-Report_2023.pdf
Forcellini, M., & Gracikova, E. (2025). 从 cognitive bias to algorithmic influence: Theoretical shifts in behavioural 财务. 日志 of Behavioural Economics and 政策, 1(1), 20–27. https://doi.org/10.55121/jbep.v1i1.766
Guttman-Kenney, B., Firth, C., 和 Gathergood, J.（2022）。 先买后付（BNPL）……透过您的信用卡. 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., 和 Fung, P.（2023）。自然语言生成中的幻觉综述。 ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730
Kahneman, D., 和 Tversky, A.（1979）。前景理论：风险条件下的决策分析。 Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Kaiser, T., & Menkhoff, L. (2017). Does financial education impact financial literacy and financial behavior, and if so, when? The World 银行 Economic 审核, 31(3), 611–630. https://doi.org/10.1093/wber/lhx018
Kolb, D. A. (2014). Experiential learning: 经验 as the source of learning and development (2nd ed.). Pearson.
Lusardi, A., 和 Messy, F. A.（2023）。金融素养的重要性及其对财务福祉的影响。 日志 of 财务 Literacy and Wellbeing, 1(1), 1–11. https://doi.org/10.1017/flw.2023.8
Lusardi, A., 和 Mitchell, O. S.（2014）。金融素养的经济重要性：理论与证据。 日志 of Economic 文献, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
Malik, M., Nasir, M., Rayyan, M., & Usman, M. (2025). Behavioural 财务 and investor decision-making: Psychological biases in stock markets. 审核 of 已施用 管理 and 社会 Sciences, 8(2), 1129–1144. https://doi.org/10.47067/ramss.v8i2.542
Mat Rahim, N., Ali, N., & Adnan, M. F. (2022). 学生' financial literacy: A digital financial literacy perspective. 全球 Conference on 商业 and 社会 Sciences Proceeding, 13(1). https://doi.org/10.35609/gcbssproceeding.2022.1(9)
OECD. (2022). 用于衡量金融素养与金融包容性的 OECD/INFE 工具包. https://doi.org/10.1787/cbc4114f-en
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 马来西亚. Information 管理 and 商业 审核, 16(3(I)S), 657–674. https://doi.org/10.22610/imbr.v16i3(i)s.4092
Panos, G. A., & Wilson, J. O. S. (2021). 财务 literacy and responsible 财务 in the 金融科技 era. Routledge. https://doi.org/10.4324/9781003169192
Sailer, M., 和 Homner, L.（2020）。学习的游戏化：一项元分析。 Educational Psychology 审核, 32(1), 77–112. https://doi.org/10.1007/s10648-019-09498-w
Wijaya, T. F. A. (2025). Artificial intelligence (AI) innovation in driving global financial inclusion: Student program to reduce inequity and support 可持续 Development Goal (SDG) 8. 已施用 商业 and Administration 日志, 4(2), 111–119. https://doi.org/10.62201/abaj.v4i02.225
World Economic Forum. (2024). The Future of Jobs 报告 2024. https://www.weforum.org/publications/the-future-of-jobs-report-2024/