AI 财务 决策 Lab (AI-FDL)

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

Primary subtheme selection. AI-FDL is positioned primarily under Subtheme 1: Ethical AI & Shariah 治理 in the 数字化 Economy, because the innovation's title and architecture foreground ethical and responsible AI in the digital economy. The ethical-AI governance framework is a substantive, integrated component rather than a superficial label.

Secondary alignment. A defensible secondary alignment is Subtheme 3: 可持续 价值 Creation, ESG & Islamic Economics, through the SDG 4 and SDG 8 contribution and the promotion of financially responsible, resilient graduates. Islamic 财务 or Shariah elements are not forced into the innovation; they are incorporated only where genuinely relevant (for example, takaful/insurance scenarios in Module 1).

The defensible novelty of AI-FDL is a 系统-level integration, not the use of AI itself. The innovation contribution is defined as the integration of eight 层 into one educational decision laboratory:

Layer 1 — 场景 引擎: realistic student financial situations. Layer 2 — 决策 引擎: captures allocation and financial choices. Layer 3 — Consequence 模拟 引擎: models potential financial consequences. Layer 4 — Behavioural 财务 引擎: detects decision patterns consistent with selected behavioural biases. Layer 5 — 财务 健康 分析 引擎: generates relevant simulated indicators. Layer 6 — AI 财务 教练: explains decisions and provides educational feedback. Layer 7 — Ethical AI Guardrail: controls scope, transparency, privacy and inappropriate financial-advice generation. Layer 8 — Learning 分析: measures progression and learning outcomes.

Every major novelty claim was tested against the question: Could a judge challenge this statement? Claims such as first in 马来西亚, only platform, revolutionary or proven are avoided unless independently 已验证. The chapter uses qualified, evidence-based language throughout.

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

ICAME 2026 创新 竞争 — Chapter in Book

Nur Syairah Ani¹*, Nur Hafizah Roslan², Nur Amirah Borhan³, Azrizal Husin⁴, Abd Razzif Abd Razak⁵, Siti Nurulaini Azmi⁶, Siti Faizah Zainal⁷ & Rafiatul Adlin Hj Mohd Ruslan⁸

¹⁻⁷Fakulti Pengurusan dan Ekonomi, Universiti Pendidikan Sultan Idris, 35000, Tg Malim, 霹雳 · ⁸Universiti Utara 马来西亚, Kuala Lumpur Campus, 50300 马来西亚 · *Corresponding email: nursyairah@fpe.upsi.edu.my

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 Artificial 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).

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 创新 技术栈.

Layer 1 — 场景 引擎: realistic student financial situations. Layer 2 — 决策 引擎: captures allocation and financial choices. Layer 3 — Consequence 模拟 引擎: models potential financial consequences. Layer 4 — Behavioural 财务 引擎: detects decision patterns consistent with selected behavioural biases. Layer 5 — 财务 健康 分析 引擎: generates relevant simulated indicators. Layer 6 — AI 财务 教练: explains decisions and provides educational feedback. Layer 7 — Ethical AI Guardrail: controls scope, transparency, privacy and inappropriate financial-advice generation. Layer 8 — Learning 分析: measures progression and learning outcomes.

创新 proposition: AI-FDL transforms financial education from learning about money into learning through financial decisions.

The proposed development of AI-FDL follows a combination of the 设计 Thinking framework and the ADDIE Instructional 设计 Model. 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.

Because the phrase Ethical AI 驱动 appears in the innovation title, ethical AI cannot remain merely a disclaimer. AI-FDL embeds a substantive Responsible AI 治理 框架 addressing ten principles.

Transparency. 学生 must know they are interacting with AI. Explainability. 反馈 should explain reasoning rather than simply provide recommendations. 人类 Oversight. Lecturers or authorised administrators should have appropriate oversight. 数据 Minimisation. Collect only information necessary for learning. 隐私政策. Protect student information. 安全. Apply reasonable controls appropriate to prototype maturity. 偏置 and Fairness. 测试 scenarios and outputs for unfair or systematically misleading recommendations. 幻觉 控制. Use 已验证 financial knowledge and appropriate grounding or rule-based safeguards. 财务 Advice 边界. AI-FDL must clearly distinguish financial education from regulated or personalised financial advice. 用户 Autonomy. The 系统 should educate rather than dictate financial choices. Accountability. 定义 responsibility for content validation and 系统 governance.

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 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 demonstrates a governance framework for educational AI, contributing to responsible and explainable AI in higher education.

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.

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.

影响 model. The impact model follows the sequence: 输入 → Activity → 输出 → 成果 → Long-Term 影响. 输入: the AI-FDL platform. Activity: financial decision simulation. 输出: repeated decision practice. 成果: improved financial understanding and decision awareness. Long-Term 影响: greater financial capability and resilience. Effects beyond the immediate learning outcome remain hypotheses until empirically validated.

The AI 财务 决策 Lab (AI-FDL) is proposed as an innovative financial education platform that integrates Artificial 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 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.

The substantive presentation should follow a strong narrative: 问题 → Real Student 场景 → AI-FDL → 实时/Prototype Demonstration → Novelty → Ethical AI → 影响 → Commercialisation → 结束 Proposition.

推荐 scene sequence and approximate timing (for a 3–5 minute video):

1. Official Intro Montage (20 seconds) — mandatory. 2. 问题 (30 seconds) — a real Malaysian student facing a financial decision (e.g., allocating a PTPTN loan). 3. AI-FDL concept (30 seconds) — what the platform is and why it 是必需的. 4. 实时/prototype demonstration (60 seconds) — show a scenario, a decision, AI feedback and the dashboard. 5. Novelty (30 seconds) — the 8-layer integration and the learning-through-decisions proposition. 6. Ethical AI (30 seconds) — the responsible-AI governance framework. 7. 影响 (20 seconds) — expected educational and behavioural outcomes. 8. Commercialisation (20 seconds) — the business model and scaling path. 9. 结束 proposition (20 seconds) — a memorable final statement.

The video should demonstrate the innovation rather than merely repeat the chapter. Use screen capture of the prototype, clear narration and key visuals for each figure.