Hybrid Human-AI Regulated Learning

Updated 15 July 2025

Hybrid Human-AI Regulated Learning is a paradigm that merges human cognitive oversight with AI scaffolding to actively support and optimize learning processes.
The approach uses bidirectional adaptation and dynamic role allocation to balance human intuition with AI precision, ensuring contextual and personalized interventions.
Empirical applications in education, autonomous systems, and personalized curriculum development demonstrate significant performance gains and improved decision quality.

Hybrid Human-AI Regulated Learning (HHAIRL) is an advanced paradigm that integrates human cognitive regulation and AI support to optimize learning and decision-making across educational, industrial, and autonomous systems domains. The approach aims to leverage the complementary strengths of human expertise and AI systems through adaptive, interactive, and contextually aware regulation of learning processes. HHAIRL frameworks explicitly focus on ensuring that learners, operators, or decision-makers remain active agents, while AI contributes targeted scaffolding, analytics, or control, resulting in improved performance, tailored outcomes, and increased trust in hybrid sociotechnical systems.

1. Foundational Principles and Theoretical Frameworks

Hybrid Human-AI Regulated Learning is grounded in theories of self-regulated learning (SRL), human-in-the-loop (HITL) AI, and hybrid intelligence. HHAIRL systems draw from established models such as Winne and Hadwin’s COPES model and Zimmerman’s cyclical model of SRL, ensuring that planning, monitoring, strategic adaptation, and evaluation are systematically observed and scaffolded (Li et al., 10 Jul 2025). The core regulatory principle is to foster a balanced interaction where AI systems support, but do not supplant, the learner’s (or operator’s) agency.

Key attributes include:

Bidirectional Adaptation: Both humans and AI agents adapt their behavior via mutual feedback loops, enabling dynamic co-regulation and learning (Huang et al., 2019).
Task and Role Allocation: Inspired by dual-process and complementary intelligence models, HHAIRL systems allocate roles and decisions according to situational expertise—humans contribute intuition, ethical oversight, and meta-cognition, while AI excels in pattern recognition, speed, and consistency (Dellermann et al., 2021).
Context and Temporality: HHAIRL frameworks address the temporal nature of learning and task execution, integrating sequential and contextual information in both human and machine models (Hooshyar et al., 22 Apr 2025).
Personalization and Equity: By analyzing trace data and learner characteristics, HHAIRL enables tailored, equitable support strategies that account for differences in SRL proficiency, AI literacy, and resource accessibility (Long et al., 31 Mar 2025).

2. HHAIRL Architectures and Methodological Approaches

Implementations of HHAIRL span a spectrum from explicit reward/regulation integration in reinforcement learning environments to generative AI-enabled scaffolding for educational applications.

a. Brain-Computer Interface and Implicit Preference Integration

Early demonstrations of HHAIRL involve hybrid brain-computer interfaces (hBCI) that decode subjective interest from neural and physiological markers (EEG, pupil dilation, gaze) and integrate these measures into deep reinforcement learning (DRL) agents. For example, by segmenting neural data into time bins and applying Fisher Linear Discriminant Analysis, systems derive implicit signals that drive AI agents to adapt task execution (e.g., automobile speed), resulting in measurably increased viewing time of user-preferred objects (Shih et al., 2017).

b. Hybrid Active Inference and Shared Generative Models

Advanced systems employ hierarchical, probabilistic models that jointly process human brain signals and environmental data, using techniques such as variational auto-encoders and predictive coding networks (Ofner et al., 2018). The system aims to minimize joint free energy via generative modeling:

$F = \mathbb{E}_{q}[\ln q(z) - \ln p(x, y, z)]$

where $x$ are sensory/environmental signals, $y$ are brain signals, and $z$ denotes shared latent causes, facilitating mutual calibration and gradual fusion of cognitive processes.

c. Multi-Agent and RL-based Delegation

In complex operational environments (e.g., autonomous vehicles or robotics), HHAIRL adopts RL-based delegation frameworks, where a manager agent decides, often at critical intervention points, which agent (human or AI) controls the system. The delegation is framed as a Markov Decision Process, optimizing under constraints to maximize performance while minimizing intervention costs (Fuchs et al., 8 Feb 2024). The general manager reward is:

$R = \mathbb{1}\{\text{goal reached}\} - \tanh\left(\delta \cdot \sum_{x \in \mathcal{E}} \rho_x \cdot c_x\right)$

where $\rho_x$ and $c_x$ quantify violation frequencies and associated penalties, respectively.

d. Generative AI and Learning Analytics for Scaffolding

Modern HHAIRL systems incorporate GenAI interfaces and granular learning trace analytics to detect SRL behaviors in real time and generate adaptive feedback (Li et al., 10 Jul 2025). For instance, the FLoRA Engine uses collaborative writing tools, multi-agent chatbots, and process mining applied to trace data to scaffold planning, monitoring, and evaluation dynamically, preventing lapses in metacognitive engagement. GenAI modules create personalized prompts based on both static (survey) and dynamic (behavioral) measures.

3. Human-AI Interaction Models and Decision Flow

The taxonomy of hybrid decision-making within HHAIRL encompasses three main paradigms (Punzi et al., 9 Feb 2024):

Human Overseer: The AI provides a preliminary output, which the human reviews and either accepts or rejects, exemplifying post hoc regulation.
Learn to Defer: The system learns an abstention or deferral policy, deferring to human judgment when model uncertainty or risk is high.
Learn Together: An iterative, collaborative interaction where both human and AI update their models reciprocally, sharing intermediate reasoning artifacts (logic, explanations) and engaging in mutual teaching.

Technical formulations include:

Predictors $f : \mathcal{X} \rightarrow \mathcal{Y}_M$ for the machine and $h : \mathcal{X} \times \mathcal{Z} \rightarrow \mathcal{Y}_H$ for the human.
Loss functions accounting for deferral:

$\mathscr{L}_{\text{defer}}(Y^*, Y_M, Y_H, \rho_M) = \mathbbm{1}_{\{\rho_M(X)=0\}}\,\mathscr{L}_M(Y^*, Y_M) + \mathbbm{1}_{\{\rho_M(X)=1\}}\,\mathscr{L}_H(Y^*, Y_H)$

4. Applications and Empirical Validation

HHAIRL has been validated across diverse domains, empirically demonstrating improvements in process regulation, engagement, and ultimate outcomes.

Education: Deployments in middle and postsecondary education show that hybrid human-AI tutoring can increase “skills proficient” attainment and learning engagement, with pronounced effects for lower-achieving or resource-constrained learners (Thomas et al., 2023).
Autonomous Systems: RL-managers in driving simulators successfully learned delegation policies that outperformed any solo agent, especially in adverse or uncertain contexts, with up to 187% performance gains over the best isolated agent (Fuchs et al., 8 Feb 2024).
Collaborative AI Training: In RL scenarios, systems learning from both human demonstration and policy correction outperformed both human and AI-alone baselines, while also reducing cognitive load on human trainers (Islam et al., 2023).
Personalized Curriculum Development: Hybrid crowdsourcing–AI platforms can construct and update personal curricula with high F1-scores for skill and topic recommendations, highlighting the scalable potential of hybrid pipelines (Tavakoli et al., 2021).
Preference Learning and RLHF: Selectively routing labeling tasks to human or AI annotators—based on predicted downstream reward model performance—yields improved accuracy (7–13% improvement) and reduced annotation costs (Miranda et al., 24 Oct 2024).

5. Challenges, Risks, and Ethical Considerations

HHAIRL introduces nontrivial challenges:

Over-Reliance and “Metacognitive Laziness”: Empirical studies show that AI scaffolds, if not carefully balanced, can precipitate reductions in metacognitive SRL activity, leading to superficial improvement (e.g., essay revision scores) without deeper knowledge transfer (Fan et al., 12 Dec 2024). Systems must prevent learners from offloading regulation entirely to AI.
Equity and Differentiated Support: Cluster analysis reveals the existence of varied learner archetypes (e.g., Master, AI-Inclined, Development, Potential groups) with differing balances of SRL and AI literacy. Equitable HHAIRL must tailor interventions to foster balanced, synergistic SRL–AI development and address disparities in resource access (Long et al., 31 Mar 2025).
Interpretability and Responsible AI: To avoid the pitfalls of black-box AI in sensitive domains, neural-symbolic AI methods are advocated to embed stakeholder knowledge, improve transparency, and support local, actionable recommendations (Hooshyar et al., 22 Apr 2025).
Governance and Interface Design: Effective HHAIRL systems must provide interfaces that transparently mediate role allocations, enable adjustable trust calibration, and support human oversight without overburdening or sidelining human partners (Dellermann et al., 2021).

6. Future Directions and Research Frontiers

Recent and ongoing research highlights several promising avenues:

Lifecycle and Sustainable Integration: Embedding energy-awareness and resource efficiency directly into training pipelines, guided interactively by both human and AI “agents” (e.g., LLMs), addresses the environmental costs of large-scale AI deployments (Geissler et al., 15 Jul 2024).
Meta-cognition and Dynamic Relational Models: Theorists advocate for models treating AI as dynamic learning partners, with bidirectional emotional and cognitive growth, and the emergence of a “third mind” in collaborative contexts (Mossbridge, 7 Oct 2024).
Full-Stack Hybrid Reasoning: Hybrid architectures increasingly centralize human judgment in the DIKW (Data–Information–Knowledge–Wisdom) spectrum, with AI providing pre-conclusive support via generative and retrieval-augmented tools, and humans retaining final evaluative and strategic authority (Koon, 18 Apr 2025).

7. Summary Table: Core Features of Representative HHAIRL Systems

System/Domain	Human Function	AI Function	Regulatory Mechanism
hBCI–DRL (Shih et al., 2017)	Goal setting, implicit interest	RL policy learning, implicit reward shaping	EEG/ocular signal integration
FLoRA (Li et al., 10 Jul 2025)	Planning/monitoring, reflection	GenAI feedback, analytics, scaffolding	Real-time trace-based adaptation
Curriculum Dev. (Tavakoli et al., 2021)	Content curation, review	Topic modeling, recommendation	Crowd/AI voting thresholds
RL Delegation (Fuchs et al., 8 Feb 2024)	Task execution, situational expertise	Policy selection, constraint vigilance	RL-based intervention manager
Preference Routing (Miranda et al., 24 Oct 2024)	Direct annotation in complex cases	Synthetic annotation, label propagation	PPM-based routing optimization
Neural-Symbolic AI (Hooshyar et al., 22 Apr 2025)	Rule formulation, theory input	Statistical learning, sequential modeling	Loss-augmented symbolic constraints