Aim and Scope

Machine Learning for Human Intelligence (MLHI) is a double-blind peer-reviewed, open-access biannual journal dedicated to advancing research at the intersection of artificial intelligence, machine learning, and human cognition. Recognized by leading academic institutions, MLHI publishes high-quality theoretical, empirical, and applied studies that explore how intelligent systems can augment and interpret human intelligence, behavior, and decision-making.

Core Objectives

  1. To foster interdisciplinary dialogue between AI/ML researchers and cognitive scientists, neuroscientists, psychologists, and domain experts.

  2. To promote innovative methodologies that bridge data-driven learning with human-centric design.

  3. To address ethical and societal challenges in deploying AI systems for human augmentation.

Scope

MLHI covers (but is not limited to) the following areas:

1. Foundational AI/ML Research

  • Neural networks, deep learning, and generative models

  • Neurosymbolic AI and hybrid reasoning systems

  • Reinforcement learning for adaptive human-AI collaboration

2. Human-Centric Intelligence

  • Cognitive and behavioral modeling using ML

  • Explainable AI (XAI) for interpretable decision-making

  • Affective computing and emotion-aware systems

3. Applied AI for Societal Impact

  • Healthcare: Predictive diagnostics, personalized therapy, and medical imaging

  • Education: Adaptive learning platforms and intelligent tutoring systems

  • Social Good: AI for climate modeling, smart cities, and equitable policymaking

4. Emerging Paradigms

  • Quantum machine learning

  • Neuromorphic computing and brain-inspired algorithms

  • Federated learning and privacy-preserving AI

Article Types

  • Original Research: Substantive technical/theoretical contributions (6,000–10,000 words).

  • Reviews: Critical surveys of emerging trends (8,000–12,000 words).

  • Case Studies: Real-world implementations with measurable impact (4,000–7,000 words).

  • Short Communications: Brief reports on novel methodologies (3,000–5,000 words).

Submission Criteria

  • Novelty: Unpublished work with significant scientific contribution.

  • Rigor: Robust methodology (theoretical/experimental) and reproducibility.

  • Interdisciplinary Relevance: Priority for studies bridging AI/ML with psychology, neuroscience, or social sciences.