Multimodal Artificial Intelligence for Early Identification of Learning Difficulties

Thabo M. Nkosi

Multimodal Artificial Intelligence for Early Identification of Learning Difficulties

Author(s)	Dr. Thabo M. Nkosi
Country	United States
Abstract	Early learning difficulties are frequently recognized only after persistent academic underachievement becomes visible, by which time students may already have experienced repeated frustration, reduced academic confidence, inappropriate instructional placement, or delayed specialist support. Artificial intelligence offers an emerging opportunity to complement teacher observation and conventional educational assessment by detecting patterns across multiple forms of learner data. This study examines the potential of multimodal artificial intelligence for the early identification of learning difficulties through a structured integrative review and conceptual analytical methodology. Multimodal systems can combine academic responses with eye movements, oral reading characteristics, handwriting behavior, interaction logs, video-derived behavior, physiological signals, and contextual educational information, thereby providing a broader representation of learning processes than single-source screening. Contemporary research demonstrates promising applications in reading difficulties, dyslexia, dysgraphia, engagement analysis, collaborative learning, and adaptive education. A 2025 systematic review of multimodal learning analytics in K–8 education identified only 14 peer-reviewed empirical studies published between 2011 and 2023 and reported continuing weaknesses in explicit data-fusion methodology, ethics, and transparency. A recent multimodal dyslexia-screening framework integrating eye-gaze, speech, and handwriting information further demonstrated the technical feasibility of multimodal risk identification. Within that framework, the independently evaluated eye-tracking classifier achieved 92.8% accuracy, an F1 score of approximately 0.93, and an area under the receiver-operating-characteristic curve of 0.99; however, these performance measures apply specifically to the eye-tracking module and should not be interpreted as validation of the complete multimodal system. The present paper proposes a Multimodal Learning-Difficulty Identification Pathway integrating low-burden screening, modality-specific feature extraction, explainable data fusion, uncertainty estimation, teacher review, and referral for formal assessment. The analysis argues that educational AI should identify patterns requiring additional attention rather than autonomously assign diagnostic labels. Responsible implementation requires representative datasets, age-appropriate consent, privacy protection, cultural and linguistic validation, explainability, human oversight, and ongoing evaluation of false-positive and false-negative consequences. Multimodal AI may therefore become a valuable component of inclusive education when it strengthens—not substitutes for—professional educational and clinical judgment.
Keywords	multimodal artificial intelligence; learning difficulties; learning disabilities; early identification; dyslexia screening; learning analytics; eye tracking; handwriting analytics; speech analysis; inclusive education
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2020
Published On	2020-02-23

View / Download PDF File

About AJAMIR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamir.com

American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Multimodal Artificial Intelligence for Early Identification of Learning Difficulties

Share this