HUMAN-COMPUTER INTERACTION AS A FRAMEWORK FOR AI-ENHANCED ESL E-LEARNING: A MIXED-METHODS STUDY

Abstract

Background
The rapid growth of digital learning platforms has transformed the way people learn English as a Second Language (ESL). However, many existing tools have failed to adequately address issues such as usability, learner engagement, and personalization.

Objectives
This study applied Human-Computer Interaction (HCI) principles to enhance the usability and accessibility of ESL platforms. It integrated Artificial Intelligence (AI) functionalities including chatbots, speech recognition, and adaptive feedback to enable personalized learning. The combined impact of HCI and AI on learner engagement, motivation, and academic achievement was evaluated.

Methods
A mixed-methods design was employed. A prototype ESL tool incorporating HCI design principles and AI features was developed and tested by ESL students. Data were collected through pre- and post-tests, usability surveys, and participant interviews.

Results
The integration of HCI and AI significantly reduced cognitive load, promoted real-time interaction, and delivered culturally responsive interfaces tailored to the diverse needs of learners. Participants demonstrated improved learner autonomy, higher motivation, and better language retention compared to those using traditional or non-AI-supported tools.

Implications
The findings provide valuable insights for educators, designers, and policymakers, guiding the development of next-generation ESL platforms that are technologically advanced, learner-centered, and accessible to diverse populations