This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t tests and Mann Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5 per cent for Italian, 66.6 per cent for Spanish, and 75.0 per cent for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy.

Thousands of university students in the UK have been caught misusing ChatGPT and other artificial intelligence tools in recent years, while traditional forms of plagiarism show a marked decline, a Guardian investigation can reveal. A survey of academic integrity violations found almost 7,000 proven cases of cheating using AI tools in 2023-24, equivalent to 5.1 for every 1,000 students. That was up from 1.6 cases per 1,000 in 2022-23. Figures up to May suggest that number will increase again this year to about 7.5 proven cases per 1,000 students – but recorded cases represent only the tip of the iceberg, according to experts. The data highlights a rapidly evolving challenge for universities: trying to adapt assessment methods to the advent of technologies such as ChatGPT and other AI-powered writing tools.

You might think that having a boy or a girl is completely up to chance. But expectant parents might be able to hazard a good guess – depending on what the father's facial features are like. Researchers wanted to find out whether certain traits in parents were linked to the sex of their firstborn. The team, from the University of Michigan, recruited 104 pairs of parents with at least one child. Both were asked to submit facial photographs which were rated for attractiveness, dominance and masculinity or femininity by university students.

Recent advancements in robots powered by large language models have enhanced their conversational abilities, enabling interactions closely resembling human dialogue. However, these models introduce safety and security concerns in HRI, as they are vulnerable to manipulation that can bypass built-in safety measures. Imagining a social robot deployed in a home, this work aims to understand how everyday users try to exploit a language model to violate ethical principles, such as by prompting the robot to act like a life partner. We conducted a pilot study involving 21 university students who interacted with a Misty robot, attempting to circumvent its safety mechanisms across three scenarios based on specific HRI ethical principles: attachment, freedom, and empathy. Our results reveal that participants employed five techniques, including insulting and appealing to pity using emotional language. We hope this work can inform future research in designing strong safeguards to ensure ethical and secure human-robot interactions.

These sensors include accelerometer, GPS/geolocation, Bluetooth, communication logs (phone and SMS), application usage and keyboard activity. Given their various sensors and the opportunities to utilise them, smartphones, the Swiss army knives of digital technology, have proven to be valuable personal sensing devices, with applications in domains such as health, education and leisure. Given their potential to track various health-related behaviours and user contexts, as well as the emergence of health apps, smartphone sensing has become a pivotal topic in digital health. This is particularly the case in digital mental health, where the concept of digital phenotyping has emerged in recent years. In short, digital phenotyping espouses the idea that the data created from our use of and interaction with digital technologies, such as smartphones, can be mined or analysed to infer behaviours and, ultimately assess mental health [1, 2]. The focus of our work in this paper is on leveraging smartphone sensing as a tool in psychology and mental health. Once raw sensor data is collected, it is typically processed into information features that can be used in statistical analyses and machine learning model construction. For instance, from raw geolocation data one, features such as total distance travelled or time spent at the most visited location can be derived. In this paper, however, we propose a novel approach to analyze smartphone sensing data.

To mitigate the difficulties of university freshmen in adapting to university life, we developed NOVI, a chatbot system based on GPT-4o. This system utilizes post and comment data from SKKU 'Everytime', a university community site. Developed using LangChain, NOVI's performance has been evaluated with a BLEU score, Perplexity score, ROUGE-1 score, ROUGE-2 score, ROUGE-L score and METEOR score. This approach is not only limited to help university freshmen but is also expected to help various people adapting to new environments with different data. This research explores the development and potential application of new educational technology tools, contributing to easier social adaptation for beginners and settling a foundation for future advancement in LLM studies.

"Your pictures and personal information have been leaked. As the university student entered the chatroom to read the message, she received a photo of herself taken a few years ago while she was still at school. It was followed by a second image using the same photo, only this one was sexually explicit, and fake. Terrified, Heejin, which is not her real name, did not respond, but the images kept coming. In all of them, her face had been attached to a body engaged in a sex act, using sophisticated deepfake technology.

The world's shortest IQ test not only reveals your intelligence but also your level of patience. The test, called a Cognitive Reflection Test (CRT), consists of three math-based questions that target a person's ability to ignore their initial gut response in favor of a more rational thought process. Many quickly assume the answers are simple, but the Yale University professor who created the exam warned it isn't as straightforward as it may seem. Professor Shane Frederick created the CRT in 2005 and only 20 to 40 percent of students who have attempted it have passed. A Yale University professor designed a Cognitive Reflection Test ( CRT) that consists of three math-based questions that target a person's ability to ignore their initial gut response in favor of a more rational thought process Mathematical brain teasers are useful in helping people develop logical thinking by promoting brain stimulation and build visual and spatial reasoning skills.

As mental health issues for young adults present a pressing public health concern, daily digital mood monitoring for early detection has become an important prospect. An active research area, digital phenotyping, involves collecting and analysing data from personal digital devices such as smartphones (usage and sensors) and wearables to infer behaviours and mental health. Whilst this data is standardly analysed using statistical and machine learning approaches, the emergence of large language models (LLMs) offers a new approach to make sense of smartphone sensing data. Despite their effectiveness across various domains, LLMs remain relatively unexplored in digital mental health, particularly in integrating mobile sensor data. Our study aims to bridge this gap by employing LLMs to predict affect outcomes based on smartphone sensing data from university students. We demonstrate the efficacy of zero-shot and few-shot embedding LLMs in inferring general wellbeing. Our findings reveal that LLMs can make promising predictions of affect measures using solely smartphone sensing data. This research sheds light on the potential of LLMs for affective state prediction, emphasizing the intricate link between smartphone behavioral patterns and affective states. To our knowledge, this is the first work to leverage LLMs for affective state prediction and digital phenotyping tasks.

Many studies have shown how ergonomically designed furniture improves productivity and well-being. As computers have become a part of students' academic lives, they will grow further in the future. We propose anthropometric-based furniture dimensions suitable for university students to improve computer laboratory ergonomics. We collected data from 380 participants and analyzed 11 anthropometric measurements, correlating them to 11 furniture dimensions. Two types of furniture were studied: a non-adjustable chair with a non-adjustable table and an adjustable chair with a non-adjustable table. The mismatch calculation showed a significant difference between furniture dimensions and anthropometric measurements. The one-way ANOVA test with a significance level of 5% also showed a significant difference between proposed and existing furniture dimensions. The proposed dimensions were found to be more compatible and reduced mismatch percentages for both males and females compared to existing furniture. The proposed dimensions of the furniture set with adjustable seat height showed slightly improved results compared to the non-adjustable furniture set. This suggests that the proposed dimensions can improve comfort levels and reduce the risk of musculoskeletal disorders among students. Further studies on the implementation and long-term effects of these proposed dimensions in real-world computer laboratory settings are recommended.