Springer Science and Business Media LLC

Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education.

The gamification of education can enhance levels of students’ engagement similar to what games can do, to improve their particular skills and optimize their learning. On the other hand, scientific studies have shown adverse outcomes based on the user’s preferences. The link among the user’s characteristics, executed actions, and the game elements is still an open question. Aiming to find some insights for this issue, we have investigated the effects of gamification on students’ learning, behavior, and engagement based on their personality traits in a web-based programming learning environment. We have conducted an experiment for four months with 40 undergraduate students of first-year courses on programming. Students were randomly assigned to one of the two versions of the programming learning environment: a gamified version composed of ranking, points, and badges and the original non-gamified version. We have found evidence that gamification affected users in distinct ways based on their personality traits. Our results indicate that the effect of gamification depends on the specific characteristics of users.

First part title: Studying the impact of gamification on learning and engagement based on the personality traits of students

The interaction with the various learners in a Massive Open Online Course (MOOC) is often complex. Contemporary MOOC learning analytics relate with click-streams, keystrokes and other user-input variables. Such variables however, do not always capture users’ learning and behavior (e.g., passive video watching). In this paper, we present a study with 40 students who watched a MOOC lecture while their eye-movements were being recorded. We then proposed a method to define stimuli-based gaze variables that can be used for any kind of stimulus. The proposed stimuli-based gaze variables indicate students’ content-coverage (in space and time) and reading processes (area of interest based variables) and attention (i.e., with-me-ness), at the perceptual (following teacher’s deictic acts) and conceptual levels (following teacher discourse). In our experiment, we identified a significant mediation effect of the content coverage, reading patterns and the two levels of with-me-ness on the relation between students’ motivation and their learning performance. Such variables enable common measurements for the different kind of stimuli present in distinct MOOCs. Our long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide students gaze-aware feedback to improve overall learning process. One key ingredient in the process of achieving a high level of adaptation in providing gaze-aware feedback to the students is to use Artificial Intelligence (AI) algorithms for prediction of student performance from their behaviour. In this contribution, we also present a method combining state-of-the-art AI technique with the eye-tracking data to predict student performance. The results show that the student performance can be predicted with an error of less than 5%.

This article provides university leaders an introduction to the emerging micro-credentials field, including a snapshot of the global landscape. Despite the accelerated interest in micro-credentials, this article also raises a fundamental strategic question for leaders at the outset: Are micro-credentials right for our university? Part I discusses the basic elements of mcro-credentials, definitions, types of micro-credentials, and affordances and barriers and various providers of micro-credentials. Part II presents a snapshot of what is happening on the global playing field and the challenges inherent in trying to standardise micro-credentials globally. The final section of the article provides some general observations by the authors, lessons from practice, and brief example of how institutions may implement a strategic reset using micro-credentials. The authors close by emphasising micro-credentials are not a panacea for resolving institutional challenges and they are unlikely to become a major revenue enhancement. They may provide strategic value in their integration with other major institutional initiatives.