Educators’ and Local Policymakers’ Interpretation and Trust of AI-Generated Recommendations in High-Uncertainty Environments

The integration of artificial intelligence (AI) into education and policy decision-making has accelerated, raising critical questions about how educators and local policymakers interpret and trust AI-generated recommendations, especially in high-uncertainty environments. Research consistently highlights that trust is a pivotal factor influencing the adoption and effective use of AI tools by teachers and policymakers (Feldman-Maggor et al., 2025; Nazaretsky et al., 2022; Viberg et al., 2024; Karran et al., 2024; Vincent‐Lancrin & Vlies, 2020).

Key determinants of trust include the transparency and explainability of AI systems, perceived benefits versus risks, prior experience, cultural context, and the alignment of AI outputs with professional judgment (Feldman-Maggor et al., 2025; Kizilcec, 2023; Nazaretsky et al., 2022; Viberg et al., 2024; Karran et al., 2024; Selten et al., 2023). While explainable AI (XAI) features can enhance understandability and trust—particularly when explanations are domain-specific—confirmation bias remains a challenge: educators are more likely to trust recommendations that align with their own expertise or expectations (Feldman-Maggor et al., 2025; Nazaretsky et al., 2021; Selten et al., 2023). Concerns about privacy, ethical use, algorithmic bias, data security, and the potential for overreliance or misalignment with educational values further complicate trust-building (Ifenthaler et al., 2024; Alzahrani, 2024; Wang, 2020; Karran et al., 2024; Renta-Davids et al., 2025; Vincent‐Lancrin & Vlies, 2020).

Policymakers often respond to uncertainty by adopting precautionary or restrictive policies, emphasizing the need for robust stakeholder engagement and evidence-based frameworks (Ifenthaler et al., 2024; Chan, 2023). Ultimately, fostering appropriate trust requires not only technical improvements but also targeted professional development, clear institutional guidelines, and ongoing dialogue among all stakeholders (Nazaretsky et al., 2022; Viberg et al., 2024; Karran et al., 2024).

A rigorous and comprehensive systematic search was conducted across multiple major scientific databases, including Semantic Scholar and PubMed. The initial search yielded a vast corpus of over 1.8 million potentially relevant records. After applying rigorous relevance filtering and removing duplicates to focus on educator and policymaker trust in AI under uncertainty, the remaining manuscripts were retained for eligibility screening based on predefined inclusion criteria. Ultimately, 50 peer-reviewed studies were included in the final synthesis.

Six unique search strategies were used to capture foundational theories, empirical studies on uncertainty, terminology variants (e.g., “algorithmic advice”), critiques/resistance perspectives, adjacent domains (e.g., healthcare), and stakeholder differences.

**Factors Influencing Trust: Transparency, Explainability & Professional Judgment** Trust is strongly influenced by how transparent and understandable an AI system is. Teachers report higher trust when provided with domain-specific explanations rather than generic or data-driven ones (Feldman-Maggor et al., 2025). Explainable AI (XAI) features that “speak” the pedagogical language of educators foster greater acceptance (Feldman-Maggor et al., 2025). However, both teachers and street-level bureaucrats (such as local policymakers) tend to trust AI recommendations primarily when they confirm their own professional judgment—a phenomenon known as confirmation bias (Nazaretsky et al., 2021; Selten et al., 2023). Explanations alone do not always increase trust if recommendations contradict users’ expertise.

**Perceived Risks: Privacy, Bias & Ethical Concerns** Concerns about privacy violations, algorithmic bias, lack of transparency (“black box” effect), data security breaches, job displacement fears, and ethical dilemmas are prevalent among both educators and policymakers (Ifenthaler et al., 2024; Alzahrani, 2024; Wang, 2020; Karran et al., 2024; Renta-Davids et al., 2025). These concerns are heightened in high-uncertainty or high-stakes contexts where decisions have significant consequences for students or communities.

**Role of Professional Development & Institutional Policy** Targeted professional development programs that demystify how AI works—especially those addressing misconceptions—can increase teachers’ willingness to adopt AI tools (Nazaretsky et al., 2022). Institutional policies play a dual role: while clear guidelines can support responsible adoption by addressing ethical use and equity concerns (Ifenthaler et al., 2024; Chan, 2023), overly restrictive or ambiguous policies may stifle innovation or shift risk onto individual educators (Tsao, 2025).

**Stakeholder Differences & Contextual Factors** Trust varies across stakeholder groups (teachers vs. administrators vs. policymakers), cultural/geographic contexts (Viberg et al., 2024), urban/rural divides (Zhang et al., 2024), levels of digital literacy (Delello et al., 2025), prior experience with technology (Deric et al., 2025), and perceived alignment between AI outputs and local needs or values (Filiz et al., 2025). Policymakers often emphasize multi-stakeholder engagement frameworks to ensure that diverse perspectives inform policy design (Ifenthaler et al., 2024; Chan, 2023).

The literature demonstrates that trust is not static; it evolves as users interact with AI systems over time—and is shaped by understandability (explainability), perceived utility/benefit versus risk/harm trade-offs, prior experience with technology or similar systems (including negative experiences), cultural context (e.g., uncertainty avoidance), institutional support structures (policies/guidelines), and ongoing professional development opportunities (Feldman-Maggor et al., 2025; Kizilcec, 2023; Nazaretsky et al., 2022; Viberg et al., 2024).

While explainable AI features can enhance understanding—and thus foster trust—confirmation bias remains a persistent barrier: both teachers and local policymakers are more likely to accept recommendations that align with their own beliefs or expertise rather than those that challenge them—even if well-explained (Nazaretsky et al., 2021; Selten et al., 2023). This dynamic underscores the importance of designing interventions not just for transparency but also for critical engagement.

Privacy concerns remain a major deterrent to trusting AI-generated recommendations—especially when sensitive student data is involved or when system opacity makes it difficult to assess fairness/bias risks (Alzahrani, 2024; Wang, 2020). Policymakers must balance innovation with robust safeguards around data protection.

Professional development programs tailored to demystify how AI works—and address common misconceptions—are effective at increasing both understanding and willingness to adopt new technologies among educators (Nazaretsky et al., 2022). However, without supportive institutional policies that clarify roles/responsibilities/ethical boundaries—and without ongoing stakeholder engagement—adoption may be inconsistent or fraught with resistance.

The matrix below shows where empirical evidence is concentrated and where critical research gaps remain.