Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state, it may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management Systems store a large amount of data that could help to generate predictive models to early identify those students in online and blended learning. The contribution of this paper is twofold: First, a new adaptive predictive model is presented based only on students' grades specifically trained for each course. A deep analysis has been performed in the whole institution to evaluate its performance accuracy. Second, an early warning system has been developed focusing on dashboards visualization for stakeholders (i.e., students and teachers) and an early feedback prediction system to intervene in the case of at-risk identification. The early warning system has been evaluated in two case studies on first-year undergraduate courses in Computer Science. We show the accuracy of the correct identification of at-risk students, the students' appraisal and the most common factors which lead to at-risk level.

Dropout is one of the major problems online higher education faces. Early identification of the dropout risk level and an intervention mechanism to revert the potential risk have been proved as the key answers to solving the challenge. Predictive modeling has been extensively studied on course dropout. However, intervention practices are scarce, sometimes mixed with mechanisms focused on course failure, and commonly focused on limited interventions driven mainly by teachers' experience. This work contributes with a novel approach for identifying course dropout based on a dynamic time interval and intervening, focusing on avoiding dropout at the assessable activity level. Moreover, the system can recommend the best interval for a course and assessable activity based on artificial intelligence techniques to help teachers in this challenging task. The system has been tested on a fully online first-year course with 581 participants from 957 enrolled learners of different degrees from the Faculty of Economics and Business at the Universitat Oberta de Catalunya. Results confirm that interventions aimed at goal setting on the ongoing assessable activity significantly reduce dropout issues and increase engagement within the course. Additionally, the work explores the differences between identification mechanisms for course dropout and failure aiming to distinguish them as different problems that learners may face.

Several tools and resources have been developed in the past years to enhance the teaching and learning process. Most of them are focused on the process itself, but few focus on the assessment process to detect at-risk learners for later acting through feedback to support them to succeed and pass the course. This research paper presents a case study using an adaptive system called Learning Intelligent System (LIS). The system includes an Early Warning System and tested in a fully online university to increase learners’ performance, reduce dropout, and ensure proper feedback to guide learners. LIS also aims to help teachers to detect critical cases to act on time with learners. The system has been tested in two first-year courses in the fully online BSc of Economics and Business at the Universitat Oberta de Catalunya. A total of 552 learners were participating in the case study. On the one hand, results show that performance is better than in previous semesters when using it. On the other hand, results show that learners' perception of effectiveness is higher, and learners are willing to continue using the system in the following semesters because it becomes beneficial for them.

Is my professional knowledge outdated? Do I have the skills needed for the new challenges of the society? What knowledge do I lack to qualify for a job I like? What universities can I address to get knowledge that improves my employment expectations? These are relevant questions that all employees have done in any moment of their life. In addition, when there are high rates of unemployment and job offers that keep unfilled, the answers to these questions are even more relevant. Answering such questions open new opportunities for employed and unemployed people, by allowing them to design a formative plan according to their skills and expectations. It also provides evidences to employers about the skills and knowledge of the society, making them more aware of the skills of their potential future employees. The companies also will have more knowledge to design the professional career of their employees according to the company needs and the knowledge and skills of their employees. This paper proposes a system that helps people by showing which knowledge and skills a person misses for a given job position and what university courses the person can take to acquire the required skills and knowledge. The system has been implemented as a recommender system that helps users in planning their life-long learning. The paper shows the architecture of the proposed system, a case study to explain how it works, a survey to validate its usefulness and usability and some conclusions after its first experimentation.