This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes. 

Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated questions as personalized feedback in an ITS. Our personalized feedback can pinpoint correct and incorrect or missing phrases in student answers as well as guide them towards correct answer by asking a question in natural language. Our approach combines cause–effect analysis to break down student answers using text similarity-based NLP Transformer models to identify correct and incorrect or missing parts. We train a few-shot Neural Question Generation and Question Re-ranking models to show questions addressing components missing in the student’s answers which steers students towards the correct answer. Our model vastly outperforms both simple and strong baselines in terms of student learning gains by 45% and 35% respectively when tested in a real dialogue-based ITS. Finally, we show that our personalized corrective feedback system has the potential to improve Generative Question Answering systems. 

Few-Shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems

s of Keynotes Learning to Learn Differently

Artificial Intelligence in Education

Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated questions as personalized feedback in an ITS. Our personalized feedback can pinpoint correct and incorrect or missing phrases in student answers as well as guide them towards correct answer by asking a question in natural language. Our approach combines cause-effect analysis to break down student answers using text similarity-based NLP Transformer models to identify correct and incorrect or missing parts. We train a few-shot Neural Question Generation and Question Re-ranking models to show questions addressing components missing in the student answers which steers students towards the correct answer. Our model vastly outperforms both simple and strong baselines in terms of student learning gains by 45% and 23% respectively when tested in a real dialogue-based ITS. Finally, we show that our personalized corrective feedback system has the potential to improve Generative Question Answering systems.

Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems

In this paper an approach for adaptive learning step calculation using ReLU transfer function in neural network is proposed. This adaptive learning rate aims to automatically choose the step size that minimizes the objective function of neural network. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The main contribution of this paper is a novel technique for adaptive learning rate calculation, if we use ReLU transfer function. The experiments in data compression datasets show that proposed approach provides better generalization capability (test set accuracy) and permits to choose the learning rate automatically. 

Adaptive Learning Rate for Unsupervised Learning of Deep Neural Networks

Personalization and active learning are key aspects to successful learning. These aspects are important to address in intelligent educational applications, as they help systems to adapt and close the gap between students with varying abilities, which becomes increasingly important in the context of online and distance learning. We run a comparative head-to-head study of learning outcomes for two popular online learning platforms: Platform A, which follows a traditional model delivering content over a series of lecture videos and multiple-choice quizzes, and Platform B, which creates a personalized learning environment and provides problem-solving exercises and personalized feedback. We report on the results of our study using pre- and post-assessment quizzes with participants taking courses on an introductory data science topic on two platforms. We observe a statistically significant increase in the learning outcomes on Platform B, highlighting the impact of well-designed and well-engineered technology supporting active learning and problem-based learning in online education. Moreover, the results of the self-assessment questionnaire, where participants reported on perceived learning gains, suggest that participants using Platform B improve their metacognition.

Comparative Study of Learning Outcomes for Online Learning Platforms

Personalization and active learning help educational systems to close the gap between students with varying abilities. We run a comparative head-to-head study of learning outcomes for two popular online platforms: Platform A, which delivers content over lecture videos and multiple-choice quizzes, and Platform B, which provides interactive problem-solving exercises and personalized feedback. We observe a statistically significant increase in the learning outcomes on Platform B. Further, the results of the self-assessment questionnaire suggest that participants using Platform B improve their metacognition.

A Comparative Study of Learning Outcomes for Online Learning Platforms

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Comparative Study of Learning Outcomes for Online Learning Platforms

A Comparative Study of Learning Outcomes for Online Learning Platforms