Multiple Users’ Experiences of an AI-Aided Educational Platform for Teaching and Learning

This chapter aims to provide a better understanding of how AI technology can be used to assist in teaching and learning at schools. The Smart Learning Partner (SLP) educational platform is based on AI technology to provide new possibilities for individualized learning and more educational resources. We used a case study approach to investigate how this AI-aided SLP platform helped to assist in teaching and learning from the multiple users’ perspectives of students, teachers, and the principal at a Chinese school. This chapter starts with a description of AI used in education and learning. The AI-aided SLP educational platform is then presented to illustrate in what ways it works to assist in teaching and learning. Based on the users’ self-reported experience, this platform can support students’ learning by providing students with individualized diagnostic feedback and assessments as well as information about the progress of their learning. In addition, students receive recommendations of micro lectures from the platform to aid improvement based on the students’ assessment results. Additionally, students can also access various micro lectures according to their interests. This platform provides teachers with reports of real-time learning situations and progress at the individual or class level. Accordingly, teachers can better adjust their pedagogical decision and teaching according to the students’ needs. The principal used the information to allocate resources and assist in curriculum planning at school. In conclusion, all users positively stated that this AI-aided SLP platform assisted in teaching and learning at school even though there were still certain challenges. At the end of the chapter, recommendations for the future platform design are offeredPeer reviewe

Teachers’ and Students’ Views of Using an AI-Aided Educational Platform for Supporting Teaching and Learning at Chinese Schools

In Chinese schools in less advanced places, there is an urgent need to improve the quality of education and educational equity. This study aims to investigate how an AI-aided educational platform can be used to provide additional teaching and learning resources to serve this need. The AI-aided educational platform used in this study is called Smart-Learning Partner (SLP), which is based on AI technology to provide new opportunities for personalized learning and more educational resources. A qualitative research method was applied in this study. We interviewed and surveyed 98 students and 32 teachers at 9 Chinese schools located in less developed areas. We used content analysis to interpret the findings based on students’ and teachers’ experiences of using the SLP platform. The data demonstrated that this kind of AI-aided educational platform was viewed by students and teachers as a useful tool in students’ learning and teachers’ work. It provided additional possibilities to students and teachers with its rich assessment tools, personalized and overall student learning analysis reports, plentiful high-quality mini-lecture videos, and recommendations from the platform based on the students’ needs for further enhancement study. However, challenges still exist. Adequate electronic devices for students are needed, especially in schools in less developed areas. Students and teachers called for user-friendly interfaces and features, social interaction aspects, and gamification mechanisms with recent online learning platforms. We conclude that based on the teachers’ and students’ views, AI-aided education platforms are useful tools for supporting teaching and learning in Chinese school

The divergent roles of social media in adolescents' academic performance

The divergent roles of social media in adolescents’ academic performance have not been confirmed, as previous studies failed to address social media use in different contexts. This study thus aims to explore the relationship between outside and inside social media behavior and academic performance in Chinese adolescents. Altogether, 560 Hong Kong adolescents (47.0% girls) were recruited and surveyed with Outside School Social Media Behavior (OSSMB) and Inside School Social Media Behavior (ISSMB). Their impulsivity and academic performances were also evaluated. Linear regression analysis and structural equation modeling (SEM) results jointly indicated that: (1) OSSMB negatively predicted the adolescents’ academic performance, whereas ISSMB positively predicted their performance; (2) the two subdimensions of ISSMB–the consuming and sharing behaviors–positively predicted academic performance; and (3) ISSMB and impulsivity played multiple mediation roles in the relationship between OSSMB and academic achievement. The results also suggested that the relationship between outside school social media behavior and academic performance may be undermined by the opposing mediation effects of inside school social media behavior and impulsivit

Exposure to Nature Sounds through a Mobile Application in Daily Life: Effects on Learning Performance among University Students

Previous studies have revealed the restorative effects of exposure to natural environments on psychological well-being and cognitive performance. Recent studies have reported the effects of exposure to nature sounds (e.g., the sounds of birds, rainfall, and waves) through a mobile application on reducing students’ mental fatigue and improving their cognitive performance. However, it remains unknown whether exposure to nature sounds through a mobile application may influence students’ learning performance. To address the gap, we conducted a study with 71 university students. During the four-week intervention, 36 students in the experimental group were exposed to nature sounds through a free mobile application for at least 30 consecutive minutes per day when working on academic-related tasks; 35 students in the control group did not have such exposure when working on similar tasks. The results show that students in the experimental group outperformed those in the control group in their engagement in deep learning, frequency of academic procrastination, and academic self-efficacy. The findings reveal the promising effects of exposure to nature sounds through a mobile application on improving students’ learning performance. The implications of the findings are discussed

The association between media multitasking and executive function in Chinese adolescents: Evidence from self-reported, behavioral and fNIRS data

This study examined the association between media multitasking and executive function in Chinese adolescents by comparing heavy/high and light/low media multitaskers, i.e., HMMs and LMMs, with self-reports, behavioral measures and functional near-infrared spectroscopy (fNIRS). The participants were 12 HMMs (media multitasking scores above the 75th percentile) and 10 LMMs (media multitasking scores below the 25th percentile) chosen from a sample of 61 adolescents. Each participant completed a self-reported questionnaire on executive function and three executive function cognitive tasks: 2-back, Color Stroop, and Number-letter Determination) while wearing the fNIRS. The results indicated that: (1) the HMMs showed more impairment in executive function than the LMMs based on questionnaire data analysis; (2) there were no significant differences between the HMMs and LMMs in their performance on the cognitive tasks; and (3) the HMMs showed greater prefrontal activation than the LMMs during the 2-back and Color Stroop tasks. These findings implied that media multitasking might be associated with the reduced effectiveness in the brain areas responsible for executive function. These findings provide evidence of the negative relationship between media multitasking and executive function; and indicated the benefits of using multiple assessment methods in studying this topic

Teachers’ and Students’ Views of Using an AI-Aided Educational Platform for Supporting Teaching and Learning at Chinese Schools

In Chinese schools in less advanced places, there is an urgent need to improve the quality of education and educational equity. This study aims to investigate how an AI-aided educational platform can be used to provide additional teaching and learning resources to serve this need. The AI-aided educational platform used in this study is called Smart-Learning Partner (SLP), which is based on AI technology to provide new opportunities for personalized learning and more educational resources. A qualitative research method was applied in this study. We interviewed and surveyed 98 students and 32 teachers at 9 Chinese schools located in less developed areas. We used content analysis to interpret the findings based on students’ and teachers’ experiences of using the SLP platform. The data demonstrated that this kind of AI-aided educational platform was viewed by students and teachers as a useful tool in students’ learning and teachers’ work. It provided additional possibilities to students and teachers with its rich assessment tools, personalized and overall student learning analysis reports, plentiful high-quality mini-lecture videos, and recommendations from the platform based on the students’ needs for further enhancement study. However, challenges still exist. Adequate electronic devices for students are needed, especially in schools in less developed areas. Students and teachers called for user-friendly interfaces and features, social interaction aspects, and gamification mechanisms with recent online learning platforms. We conclude that based on the teachers’ and students’ views, AI-aided education platforms are useful tools for supporting teaching and learning in Chinese school

How to harness the potential of ChatGPT in education?

Technological advancements, particularly in the field of artificial intelligence (AI) have played an increasingly important role in transforming education. More recently, ground-breaking AI applications like ChatGPT have demonstrated the potential to bring radical changes to the educational landscape due to their capability to understand complex questions, generate plausible responses and human-like writing, and assist with the completion of complex tasks. However, ChatGPT has limitations in the quality of its output, such as the inclusion of inaccurate, fabricated and biased information and the lack of critical thinking and in-depth understanding. The combinations of these capabilities and limitations along with external factors (e.g., the growing demand for personalized learning support, the irresponsible and unethical use of AI) presents a range of opportunities and challenges to the potential use of ChatGPT in education. This paper presents a thorough SWOT (strength, weakness, opportunity, threat) analysis of ChatGPT, based on which we propose how ChatGPT can be properly integrated into teaching and learning practice to harness its potential in education

<i>Habit-DisHabit</i> Design with a Quadratic Equation: A Better Model of the Hemodynamic Changes in Preschoolers during the Dimension Change Card Sorting Task

General linear modeling (GLM) has been widely employed to estimate the hemodynamic changes observed by functional near infrared spectroscopy (fNIRS) technology, which are found to be nonlinear rather than linear, however. Therefore, GLM might not be appropriate for modeling the hemodynamic changes evoked by cognitive processing in developmental neurocognitive studies. There is an urgent need to identify a better statistical model to fit into the nonlinear fNIRS data. This study addressed this need by developing a quadratic equation model to reanalyze the existing fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected from the mixed-order design Dimensional Change Card Sort (DCCS) task and verified the model with a new set of data with the Habit-DisHabit design. First, comparing the quadratic and cubic modeling results of the mixed-order design data indicated that the proposed quadratic equation was better than GLM and cubic regression to model the oxygenated hemoglobin (HbO) changes in this task. Second, applying this quadratic model with the Habit-DisHabit design data verified its suitability and indicated that the new design was more effective in identifying the neural correlates of cognitive shifting than the mixed-order design. These findings jointly indicate that Habit-DisHabit Design with a quadratic equation might better model the hemodynamic changes in preschoolers during the DCCS task