AI chatbots and LLMs have made a significant impact in a short time. Despite their benefits, they pose serious threats to academic integrity and ethics by generating human-like text, which is very hard to detect. Various AI-detection tools have been developed to tackle this issue. However, their effectiveness is questionable. This study investigates the performance of four AI-detection tools (Turnitin, ZeroGPT, GPTZero, and Writer AI) in detecting AI-generated text. That text was generated using three LLMs (ChatGPT, Perplexity, and Gemini). Furthermore, three adversarial techniques (edited through Grammarly, paraphrased through Quillbot, and 10%-20% editing by a human expert) were applied to see their effects on the performance of AI-detection tools. Turnitin turned out to be the most accurate and consistent one, with a 100% AI score even with the adversarial techniques. ZeroGPT and GPTZero also reported relatively high AI scores, especially with the original files and the first and third adversarial techniques. Among the three adversarial techniques, paraphrasing through Quillbot affected the performance of three AI-detection tools (ZeroGPT, GPTZero, and Writer AI) the most. Among the three LLMs, text generated through Perplexity was more accurately detected, while Gemini-generated text showed a relatively lower AI score. What was the most note-worthy was the fact that in many cases, even when the text was generated through the same LLM, and detected through the same AI-detection tool; different files showed different AI scores, further highlighting the inconsistencies among AI-detection tools.

The current study aimed to explore the effect of Brain-Based Learning on students' intrinsic motivation (IM) to learn and perform in mathematics. Owing to the educational implications of Neuroscience, the researchers planned the mixed-methods experimental study with a convergent parallel research design. The participants were eighth-graders enrolled in a boy's public secondary school in the district Kasur of Pakistan in the academic year 2021-2022. Students were taught mathematics using the traditional lecture method in the baseline phase (A) and withdrawal phase (A). In contrast, in the treatment phase (B), they were taught mathematics with activities based on the BBL approaches and principles. The mathematics motivation scale and observation (field notes) were used to collect quantitative and qualitative data simultaneously. Data were analysed using one-way repeated measure ANOVA and thematic analysis. The researchers revealed that BBL significantly affects students' IM to learn and perform in mathematics. After qualitative analysis, it was found that visual story-telling, role-playing, i-Think maps, back-to-board, and Kick Me Poison Box are the most effective BBL-based activities which keep them engaged, provide emotional support and contribute to students' IM to learn and perform in mathematics. The teachers were suggested to develop BBL-based activities to teach mathematics and to influence students' psychology in the schools.

Neurosciences is emerging as an interdisciplinary field that is making progress toward having a meaningful impact on school psychology. The present study was designed to integrate neuroscience with the brain's capacity to affect students' self-efficacy by deploying neuroscience-based instructional practices for mathematics. This experimental study aimed to examine how Brain-Based Learning (BBL) influences elementary school students' self-efficacy to learn and perform in mathematics. The A-B-A research design was used for the current single-subject investigation. Thirty-nine eighth-graders from a public secondary school participated in the study. Mathematics Motivation Scale (MMS) was used to collect data from respondents throughout the study stages. One-way repeated-measures ANOVA and visual analysis were deployed for the analysis of data. The results reveal that in the baseline phase (A), students' self-efficacy mean scores (M1 = 3.63, M2 = 3.58, M3 = 3.64) were lower than in the treatment phase (B) mean scores (M4 = 4.64, M5 = 4.75, M6 = 4.70). Mean scores (M7 = 4.12, M8 = 4.06, M9 = 4.00) decreased from treatment phase (B) to withdrawal phase A. The results indicate that significance rise of self-efficacy in the treatment phase (B) was due to the intervention applied to students. The use of BBL-based activities like visual imaging, role playing, and brainstorming were suggested to enhance students’ self-efficacy in mathematics classes.

The study was intended to explore the psychological bases of Teacher-Student Interaction (TSI) in the classroom. Moreover, differences in five psychological bases, emotional, conduct, hyperactivity, inattention, and peer interaction across teachers' gender, qualification, and teaching experience, were also the study's purpose. A cross-sectional quantitative survey study was designed for this purpose. The participants of the study were secondary school teachers from the Gujranwala district. A self-developed Teacher-Student Interaction Scale (TSIS) was used to collect data from 285 respondents. Data were analysed using mean, standard deviation, one sample, independent sample t-test, and Analysis of Variance (ANOVA). It was found that conduct and emotional bases are teachers' most frequently used psychological bases to develop TSI, while peer interaction is comparatively less contributing base. It was also revealed that there were insignificant differences regarding psychological bases across gender and qualification of the teachers. Furthermore, there was a contradiction in using psychological bases like emotional, hyperactivity, and the total scale across teachers' teaching experience. Hence, we suggest that future researchers conduct more in-depth studies on STIs to find the factors that lead to similar bases across gender, qualification, and experience.