AI in Education: A Comparative Study of Intelligent Systems and Their Impacts on Modern Learning Environments

Artificial intelligence (AI) is reshaping e-learning, especially in technical and industrial training. Yet the literature still lacks cross-platform evidence on how different tools compare in pedagogy, scalability and ethics. Addressing this gap, we present a structured comparison of six leading AI platforms-Duolingo, Squirrel AI, DreamBox, Gradescope, Knewton Alta and ChatGPT-examining their algorithms, personalization depth, and instructional impact. Results show that reinforcementlearning tutors (Squirrel AI, DreamBox) deliver the highest personalization but offer limited model transparency, while ruleguided systems (Gradescope) scale well but adapt only modestly. ChatGPT provides broad subject coverage and cloud-level scalability, yet remains vulnerable to prompt bias and medium explainability. We also identify systemic challenges around data privacy, algorithmic fairness and equitable access. By synthesizing strengths and limitations, the study offers actionable guidance for educators, developers and policymakers seeking to deploy scalable, pedagogically grounded and ethically responsible AI in industrial e-learning contexts, and outlines future directions for standardized evaluation, built-in ethics, and policy frameworks.