AI Financial Decision Lab (AI-FDL)

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

Primary subtheme selection. AI-FDL is positioned primarily under Subtheme 1: Ethical AI & Shariah Governance in the Digital 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: Sustainable Value Creation, ESG & Islamic Economics, through the SDG 4 and SDG 8 contribution and the promotion of financially responsible, resilient graduates. Islamic finance 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 system-level integration, not the use of AI itself. The innovation contribution is defined as the integration of eight layers into one educational decision laboratory:

Layer 1 — Scenario Engine: realistic student financial situations. Layer 2 — Decision Engine: captures allocation and financial choices. Layer 3 — Consequence Simulation Engine: models potential financial consequences. Layer 4 — Behavioural Finance Engine: detects decision patterns consistent with selected behavioural biases. Layer 5 — Financial Health Analytics Engine: generates relevant simulated indicators. Layer 6 — AI Financial Coach: explains decisions and provides educational feedback. Layer 7 — Ethical AI Guardrail: controls scope, transparency, privacy and inappropriate financial-advice generation. Layer 8 — Learning Analytics: 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 Malaysia, only platform, revolutionary or proven are avoided unless independently verified. The chapter uses qualified, evidence-based language throughout.

AI Financial Decision Lab (AI-FDL): An Ethical AI-Powered Financial Decision Simulation Platform for Malaysian University Students

ICAME 2026 Innovation Competition — 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, Perak · ⁸Universiti Utara Malaysia, Kuala Lumpur Campus, 50300 Malaysia · *Corresponding email: nursyairah@fpe.upsi.edu.my

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).

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.

Layer 1 — Scenario Engine: realistic student financial situations. Layer 2 — Decision Engine: captures allocation and financial choices. Layer 3 — Consequence Simulation Engine: models potential financial consequences. Layer 4 — Behavioural Finance Engine: detects decision patterns consistent with selected behavioural biases. Layer 5 — Financial Health Analytics Engine: generates relevant simulated indicators. Layer 6 — AI Financial Coach: explains decisions and provides educational feedback. Layer 7 — Ethical AI Guardrail: controls scope, transparency, privacy and inappropriate financial-advice generation. Layer 8 — Learning Analytics: measures progression and learning outcomes.

Innovation 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 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.

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

Transparency. Students must know they are interacting with AI. Explainability. Feedback should explain reasoning rather than simply provide recommendations. Human Oversight. Lecturers or authorised administrators should have appropriate oversight. Data Minimisation. Collect only information necessary for learning. Privacy. Protect student information. Security. Apply reasonable controls appropriate to prototype maturity. Bias and Fairness. Test scenarios and outputs for unfair or systematically misleading recommendations. Hallucination Control. Use verified financial knowledge and appropriate grounding or rule-based safeguards. Financial Advice Boundary. AI-FDL must clearly distinguish financial education from regulated or personalised financial advice. User Autonomy. The system should educate rather than dictate financial choices. Accountability. Define responsibility for content validation and system 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 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.

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.

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

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.

The substantive presentation should follow a strong narrative: Problem → Real Student Scenario → AI-FDL → Live/Prototype Demonstration → Novelty → Ethical AI → Impact → Commercialisation → Closing Proposition.

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

1. Official Intro Montage (20 seconds) — mandatory. 2. Problem (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 is needed. 4. Live/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. Impact (20 seconds) — expected educational and behavioural outcomes. 8. Commercialisation (20 seconds) — the business model and scaling path. 9. Closing 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.