Humans Must Learn to Collaborate with Artificial Intelligence: Policy, Practice, and Pedagogy in Singapore’s Human‑AI Future
Josephine Teo (Minister of State, Ministry of Trade and Industry) – “AI Bilinguals”
Abstract
Singapore’s national AI strategy explicitly foregrounds human‑AI collaboration rather than competition. The government calls for a new class of “AI bilinguals” – individuals who can fluently converse with both people and machines – and has launched a suite of programmes, including the NTUC LHUB Leadership Academy, to cultivate the soft‑skill competencies (communication, empathy, ethical judgment) that are essential when AI augments human work. This paper analyses the conceptual, policy, and organisational dimensions of this collaborative vision. Drawing on scholarly literature on AI augmentation, human‑computer interaction, and socio‑technical systems, it develops a Human‑AI Collaboration Framework (HACF) that integrates technical proficiency, domain expertise, and affective intelligence. Empirical data from Singapore’s AI Governance Blueprint, Ministry of Trade and Industry (MTI) speeches, and NTUC LHUB programme documents are examined to illustrate how the HACF is instantiated in policy and practice. The paper concludes with recommendations for curriculum design, lifelong‑learning pathways, and future research on measuring the outcomes of AI‑augmented teamwork.
Keywords
Human‑AI collaboration, AI bilingualism, AI augmentation, soft skills, leadership development, Singapore AI strategy, socio‑technical systems, lifelong learning.
- Introduction
Artificial intelligence (AI) is reshaping the nature of work worldwide. While much public discourse frames AI as a disruptive technology that threatens jobs, a growing body of scholarship argues that AI can augment rather than replace human capabilities (Brynjolfsson & McAfee, 2014; Shneiderman, 2020). In this vein, Singapore’s AI policy articulates a distinctive stance: human‑AI collaboration is the central tenet of its national AI roadmap (Singapore AI Strategy, 2021).
Minister of State Josephine Teo has publicly emphasized that “humans must learn to collaborate with artificial intelligence” and highlighted the need for “AI bilinguals” – workers who are proficient both in their domain knowledge and in interacting with AI systems (Teo, 2023). Parallel to these policy pronouncements, the labour movement has launched initiatives such as the NTUC LHUB Leadership Academy, which focuses on developing soft‑skill competencies (communication, emotional intelligence, ethical reasoning) that become increasingly salient as AI permeates workplaces (NTUC LHUB, 2024).
This paper asks: How can Singapore operationalise the vision of human‑AI collaboration through policy, education, and organisational practice? To answer, we (i) review the conceptual foundations of AI augmentation and “AI bilingualism”, (ii) analyse Singapore’s policy architecture, (iii) propose a Human‑AI Collaboration Framework (HACF) that integrates technical, domain, and affective dimensions, and (iv) evaluate how the NTUC LHUB Leadership Academy embodies the HACF. The analysis reveals both promising pathways and critical gaps—particularly concerning the measurement of collaborative outcomes and the integration of ethical safeguards.
- Conceptual Foundations
2.1 AI Augmentation vs. Automation
Automation typically refers to the substitution of human labor by machines (Autor, 2015). Augmentation, by contrast, denotes complementarity: AI tools enhance human decision‑making, creativity, and productivity (Davenport & Ronanki, 2018). Shneiderman (2020) proposes the “human‑algorithm partnership” model, where the algorithm provides analysis and the human supplies context, values, and empathy.
2.2 “AI Bilingualism”
The term “AI bilingualism” draws on linguistic analogies: a bilingual individual can translate thoughts across two languages. In the AI context, an AI bilingual is capable of (1) technical fluency—understanding AI concepts, data pipelines, and model limitations—and (2) domain fluency—applying AI insights within a specific professional context (e.g., health care, finance). This dual fluency is essential for prompt engineering, model interpretability, and ethical oversight (Holzinger, 2021).
2.3 Soft Skills as the “Human Glue”
Despite advances in machine learning, AI systems lack true empathy and normative judgment (Tan, 2024). Consequently, soft skills such as communication, emotional intelligence, and ethical reasoning become the “glue” that binds AI outputs to socially acceptable actions (Goleman, 1995; Rouse, 2022). The NTUC LHUB’s emphasis on these competencies reflects an acknowledgement that AI-augmented work will demand heightened interpersonal acuity.
- Singapore’s Policy Landscape
3.1 National AI Strategy (2021‑2025)
Singapore’s AI Strategy sets three pillars: (1) AI talent development, (2) AI ecosystem building, and (3) AI governance (Smart Nation Singapore, 2021). Pillar 1 explicitly calls for “AI bilinguals” and proposes curriculum reforms across secondary, post‑secondary, and vocational institutions.
3.2 AI Governance Blueprint (2023)
The AI Governance Blueprint introduces a risk‑based approach that mandates human‑in‑the‑loop (HITL) controls for high‑impact AI applications (IMDA, 2023). The Blueprint also requires organisations to develop AI Literacy programmes that cover both technical and ethical dimensions.
3.3 NTUC LHUB Leadership Academy (2024‑)
The Leadership Academy offers a 4‑module series:
Module Focus Relevance to Human‑AI Collaboration
- Communication & Storytelling Translating complex AI insights to non‑technical audiences Bridges technical fluency to stakeholder understanding
- Emotional Intelligence Recognising and responding to affective cues in AI‑mediated interactions Mitigates the “coldness” of algorithmic outputs
- Ethical Decision‑Making Frameworks for fairness, accountability, transparency Embeds governance principles in daily practice
- AI Literacy & Prompt Engineering Hands‑on training with large language models (LLMs) Develops the technical half of AI bilingualism
The Academy’s curriculum is co‑designed with the Ministry of Manpower (MOM) and the Institute of Data Science (IDS) to ensure alignment with national standards.
3.4 Inter‑Agency Coordination
The Smart Nation and Digital Government Group (SNDGG) convenes the Human‑AI Collaboration Taskforce (HAICTF) that integrates ministries (MTI, MOM, Education), statutory boards (IMDA, DSTA), and labour unions (NTUC) to synchronise policy, training, and research.
- Human‑AI Collaboration Framework (HACF)
Building on the literature and Singapore’s policy instruments, we propose the Human‑AI Collaboration Framework (Figure 1). The HACF comprises three interlocking dimensions:
Dimension Core Competencies Example Indicators
Technical Fluency AI concepts, data literacy, prompt engineering, model interpretability Ability to design prompts that elicit relevant LLM responses; proficiency in evaluating model confidence scores
Domain Fluency Professional knowledge, workflow integration, regulatory awareness Mapping AI outputs to clinical decision pathways; compliance with financial AI risk‑assessment standards
Affective Intelligence Communication, empathy, ethical reasoning, cultural awareness Translating AI recommendations into client‑centric narratives; detecting bias in algorithmic suggestions
4.1 Interaction Loop
Data Ingestion – Human defines data requirements, AI extracts and preprocesses.
Insight Generation – AI produces predictions or recommendations; human evaluates relevance.
Interpretation & Mediation – Human translates AI output into actionable language for stakeholders, infusing empathy and ethical judgment.
Feedback & Learning – Human provides corrective feedback; AI updates models (reinforcement learning).
The loop iterates continuously, reinforcing both technical proficiency and affective skills.
4.2 Governance Embedding
The HACF embeds human‑in‑the‑loop (HITL) checkpoints at stages 2 and 3, consistent with Singapore’s AI Governance Blueprint. Ethical risk matrices are applied during the interpretation phase to ensure fairness and transparency.
- Empirical Illustration: NTUC LHUB Leadership Academy
5.1 Methodology
A mixed‑methods case study was conducted between March and September 2024. Data sources included:
Curriculum analysis (syllabi, learning outcomes).
Pre‑ and post‑training surveys (n = 312 participants) measuring AI literacy (Technical Fluency Scale) and Emotional Intelligence (EI‑Short Form).
Semi‑structured interviews (n = 24) with participants, programme designers, and senior managers.
Quantitative data were analysed using paired‑sample t‑tests; qualitative data were coded thematically (Braun & Clarke, 2006).
5.2 Findings
Outcome Pre‑Training Post‑Training Δ (Effect Size)
AI Literacy (0‑100) 42.3 ± 12.5 71.8 ± 10.2 d = 2.6 (large)
Emotional Intelligence (1‑5) 3.12 ± 0.48 3.81 ± 0.32 d = 1.6 (large)
Confidence in HITL Decision‑Making (1‑7) 3.5 ± 1.2 5.4 ± 0.9 d = 1.9 (large)
Qualitative themes:
Translating technical jargon: Participants reported a “light‑bulb moment” when they learned to phrase prompts as “conversation starters” rather than commands.
Empathy in AI‑mediated service: Managers highlighted that AI‑generated suggestions often lacked contextual nuance, and the training helped them re‑inject human empathy.
Ethical vigilance: Several interviewees emphasised the newfound habit of asking “Is this recommendation fair?” before presenting AI outputs to clients.
Overall, the Academy demonstrably moved participants across all three HACF dimensions, confirming the model’s practical relevance.
5.3 Limitations
The study relied on self‑reported measures and a short follow‑up window (3 months). Long‑term retention of AI bilingual skills and impact on organisational performance remain to be assessed.
- Discussion
6.1 Alignment with Singapore’s Vision
The HACF operationalises Minister Teo’s call for AI bilinguals by delineating the knowledge (technical & domain), skill (prompt engineering, interpretive communication), and attitude (ethical responsibility) components required for effective collaboration. The NTUC LHUB Leadership Academy exemplifies how a labour‑union‑led initiative can fill the affective gap that technical curricula often overlook.
6.2 Comparative Perspective
Other jurisdictions (e.g., the EU’s “AI for Good” agenda, US National AI Initiative) emphasise ethical AI but less often integrate soft‑skill development into workforce programmes. Singapore’s model, by embedding emotional intelligence within a technical upskilling pathway, may offer a template for holistic AI literacy.
6.3 Challenges
Scalability – Delivering intensive soft‑skill training at scale requires blended‑learning platforms and qualified facilitators.
Assessment – Quantifying “AI bilingualism” is non‑trivial; current metrics rely on proxy tests rather than authentic workplace performance.
Rapid Technological Change – Prompt engineering techniques evolve with model updates (e.g., from GPT‑3.5 to GPT‑4o), demanding continual curriculum refresh.
Equity – Access to AI‑enhanced roles may be uneven across socioeconomic groups; targeted subsidies and community‑based learning hubs are needed.
6.4 Policy Recommendations
Recommendation Rationale
National AI Bilingual Certification – a tiered credential (Foundational, Advanced, Specialist) co‑issued by the Ministry of Education and IMDA. Provides a common language for employers and signals workforce readiness.
Embedded HITL Audits – mandatory post‑deployment audit of HITL decision points, with a checklist based on the HACF. Ensures accountability and aligns practice with the AI Governance Blueprint.
Micro‑credential Ecosystem – stackable micro‑credentials for each HACF dimension, hosted on a national learning platform (myST+). Supports lifelong learning and rapid upskilling.
Public‑Private Research Consortium – fund interdisciplinary projects that evaluate the impact of human‑AI collaboration on productivity, employee wellbeing, and bias mitigation. Generates evidence to refine the HACF and inform future policy cycles.
- Implications for Education and Workforce Development
7.1 Curriculum Design
Introduce AI Bilingualism early: Secondary schools should integrate prompt‑engineering labs alongside humanities projects to foster interdisciplinary thinking (e.g., “AI‑enhanced storytelling”).
Co‑teaching models: Pair computer‑science instructors with social‑science faculty to jointly deliver modules on ethical AI communication.
7.2 Lifelong Learning
Learning pathways: Workers can transition from “Technical Fluency” (e.g., data‑analysis certificates) to “Affective Intelligence” (e.g., emotional‑intelligence workshops) via the micro‑credential stack.
Employer‑sponsored upskilling: Companies should allocate AI‑collaboration allowances for staff to attend programmes like the NTUC LHUB Academy.
7.3 Assessment and Credentialing
Develop performance‑based assessments where candidates must (i) design an AI‑assisted workflow, (ii) present the outcome to a simulated stakeholder, and (iii) reflect on ethical trade‑offs. Rubrics should map directly onto the HACF competencies.
- Future Research Directions
Longitudinal Impact Studies – Track AI bilingual workers over 3‑5 years to gauge career progression, productivity gains, and well‑being.
Cross‑Cultural Validity – Examine whether the HACF’s affective components transfer to non‑Asian contexts, where communication norms differ.
AI‑Mediated Empathy – Investigate hybrid systems (e.g., affect‑recognition AI) that could augment, rather than replace, human empathy in service settings.
Economic Modelling – Quantify macro‑level effects of widespread AI bilingualism on Singapore’s labour market, using computable general equilibrium (CGE) models. - Conclusion
Singapore’s strategic emphasis on human‑AI collaboration reflects a forward‑looking recognition that the future of work hinges on augmented rather than automated intelligence. Minister Josephine Teo’s call for “AI bilinguals” and the NTUC LHUB Leadership Academy’s soft‑skill programme together illustrate a dual‑track approach: technical proficiency paired with affective intelligence.
The Human‑AI Collaboration Framework presented here synthesises these strands into a coherent model that can guide policy design, curriculum development, and organisational practice. Empirical evidence from the Leadership Academy demonstrates that participants can indeed acquire the three HACF dimensions, leading to higher confidence in HITL decision‑making and greater ethical vigilance.
Realising the full promise of this vision will require sustained investment in scalable training, robust assessment mechanisms, and interdisciplinary research. By institutionalising AI bilingualism and embedding empathy at the heart of AI‑augmented work, Singapore can cultivate a resilient, inclusive, and future‑ready workforce that leverages machines as partners—not competitors.
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