Explore the vast range of titles available.

The present study is an extension of studies that measure pre-service teachers’ Technological Pedagogical Content Knowledge (TPACK) confidence. It provides new perspectives on pre-service teachers’ TPACK by shifting the focus to concrete concerns and strengths indicated by pre-service teachers. The target group consists of a cohort of first-year pre-service teachers (N = 86) from a Finnish university. The data used in this study were 86 lesson plans with integrated technology written by first-year pre-service teachers, with a specific section where students outlined their confident and challenging areas in the lesson plan. These sections were analysed quantitatively through the theoretical lens of TPACK. Four TPACK areas were found confident, challenging or both confident and challenging for students. For these first-year pre-service teachers, pedagogical knowledge played the most important role, and the outcomes concretize specific aspects of pedagogical knowledge that can be addressed to develop TPACK in teacher education. The results provide important perspectives on pre-service-teachers’ development of TPACK, revealing the important position of pedagogical knowledge and detailed perspectives on how pre-service teachers view their readiness to use ICT in education.

This study examines the utilization of ChatGPT by pre-service mathematics teachers during the 5E lesson planning process, focusing on its affordances, constraints, and potential as a supportive tool in education. Twenty-one pre-service mathematics teachers, selected through purposive sampling, participated in the study. Data collection included 5E lesson plans, ChatGPT interaction transcripts, and participant interview responses, which were analyzed using content analysis within the framework of the instrumental approach. ChatGPT was utilized for lesson planning across categories addressing instructional and pedagogical content, encompassing fourteen distinct purposes distributed across the stages of the 5E lesson plan design: five for the engagement stage, five for exploration, six for explanation, five for elaboration, and four for evaluation. Interviews revealed both the affordances and constraints of ChatGPT, noting that while it provided creative and practical ideas, it often required well-structured prompts to yield relevant results. Despite its challenges, participants expressed their intention to use ChatGPT professionally to enhance lesson planning and instructional practices. ChatGPT is a valuable yet evolving tool for fostering innovation and efficiency in mathematics education, with implications for teacher training and future research on AI integration.

Background Lesson plans are structured outlines that enhance clarity, focus and learning engagement in medical education. Sharing pre lecture lesson plans (PLPs) may improve preparedness, attention and satisfaction among students. This study aimed to assess undergraduate medical students’ perceptions of PLPs and validate a new scale to measure these perceptions. Methods A cross-sectional study was conducted among 251 s year MBBS students using a 11-item questionnaire on a 5-point Likert scale. Item analysis, exploratory factor analysis (EFA) was applied to assess validity of questionnaire of pre-lecture plan. Reliability was measured using Cronbach’s α. Criterion validity was examined using Spearman’s correlation between individual domains and total PLP-UMS scores. Results EFA yielded an 11-item, three factor structure (participation, attitude, satisfaction) explaining 71.9% of total variance. Reliability analysis demonstrated good internal consistency (α = 0.773,0.765,0.714) for all domains. Inter-factor correlations indicated strong associations between participation and attitude( r  = 0.789) and moderate associate with satisfaction( r  = 0.516 − 0.491). the majority of students agreed that PLP was beneficial and were satisfied that pre-lecture lesson plan enhanced their participation and learning during lectures. Conclusions PLP-UMS is valid and reliable tool to measure students’ perception of lesson plans. Pre-lecture lesson plans significantly improve students’ preparedness, confidence, engagement and satisfaction, supporting their better adoption in undergraduate medical curriculum.

The current study aimed to sightsee the effectiveness of Mathematical knowledge for teaching (MKT) through lesson planning and technological pedagogical content knowledge (TPACK) on Rwandan mathematics tutors in teachers' training colleges. A quasi-experimental research design was used with a control and an experimental group. The experimental group received the intervention on developing MKT through the lesson plan and TPACK, while the control group used a standardized format lesson plan templates as their primary method for preparing instructional materials. In both groups, we analyzed 88 lesson plans in three stages (before, during, and after intervention). The collected lessons were analyzed using a standardized and validated Lesson Plan Analysis Protocol (LPAP). The result revealed that, before the intervention, teachers in both groups struggled with preparing an effective lesson, with an average of 57.8% and 54% in the control and experimental groups, respectively, indicating that the prepared lesson could not be effectively taught. During the intervention, tutors in the experimental group were facilitated to improve their lesson preparation. In this regard, the average was increased to 61.37% in the experimental group, compared to 57.67% in the control group. Moreover, the post-intervention results revealed significant divergence in the two groups' performance, with an average of 82.40% (indicating a very good lesson plan that can be taught) in the experimental group and 59.73% (a fair lesson plan) in the control group. The inferential results showed a statistically significant difference in lesson preparation between Mathematics tutors in the experimental and control groups ( p  < 0.05) in favor of the experimental group during and after intervention. The research underscores the need for educational stakeholders and continuous professional development organizers to incorporate TPACK in lesson planning during teacher training to promote a more comprehensive and forward-looking approach to tutor preparation.

The ICT-based learning model has been a catalyst in the field of modern education to teach higher-order thinking skills (HOTS). However, a few studies promoted technology-based HOTS learning to advance pre-service teachers’ ability in devising HOTS-based lesson plans. This study aimed to examine how the HOTS-Link mobile learning application assisted biology pre-service teachers in devising HOTS-based lesson plans and describe their responses on the utility of the application. The study used a descriptive-quantitative research approach with an ADDIE research design, especially in the implementation stage. The data were obtained using documentation of learning outcomes and questionnaires completed by 20 biology pre-service teachers. The results showed that the HOTS-Link mobile learning application could increase pre-service teachers’ ability in devising HOTS-based lesson plans. Another finding portrayed that all pre-service teachers conveyed a positive response toward the easy usage of the application. The present study implied that the HOTS-Link mobile learning application could be used by biology teachers to create HOTS-based lesson plans, especially for the Indonesian curriculum.

This paper investigated the integration of a Lesson Study Model (LSM) into distance STEM education during the COVID-19 pandemic. The study focused on six dimensions: (1) STEM education in distance learning, (2) Lesson Study (LS), (3) lesson planning processes, (4) challenges of lesson planning, (5) evaluation and assessment methods, and (6) strategies, methods, and techniques. The sample consisted of 24 science teachers recruited using criterion sampling, which is a purposive sampling method. A case study, which is a qualitative research method, was the design of choice. Data were collected through interviews, videotapes, and expert observations. The data were analyzed using inductive content analysis. Themes, categories, and codes were developed in accordance with the research purpose. Participants had positive opinions about the LSM, STEM education, and distance learning. Participants stated that the LSM activities within distance learning contributed to pedagogy and content knowledge in the STEM education process. The challenges faced by participants were unfavorable environmental conditions, time management issues, and a lack of knowledge and experience in lesson planning. Expert observations and videotapes corroborate these results, indicating that the LSM integrated with STEM education results in higher quality STEM lesson planning and teaching. Moreover, distance learning platforms are promising ways to ensure the professional development of teachers during the pandemic.

This study examines how student mindset influences the use of artificial intelligence (AI) tools, specifically ChatGPT, in lesson planning for sexual health education within a preschool teacher training course. With rising administrative demands in early childhood education, AI offers potential support in streamlining instructional design. The research focuses on how growth, fixed, and mixed mindsets shape students’ adoption, perceptions, and effectiveness of AI-assisted lesson planning among 45 undergraduate students. Participants developed lesson plans both with and without ChatGPT, followed by reflection on their experiences. Findings show that while students initially held clear preferences, many transitioned toward a hybrid approach after exploring the tool’s benefits and limitations. Growth mindset students preferred working independently but were open to AI as a supplemental aid. Fixed mindset students often began by relying on ChatGPT but shifted away after encountering its limitations. Mixed mindset students displayed the greatest adaptability, commonly blending AI input with personal insights. Students praised ChatGPT for enhancing creativity and saving time but criticized its lack of contextual sensitivity and emotional nuance. Overall, a blended method, merging AI support with human expertise, was most favored. The study underscores the importance of promoting flexible, growth-oriented mindsets and AI literacy in teacher education to foster effective, ethical integration of technology in the classroom.

Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption. This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student's interest area and career goals. Students' success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students' experiences was also collected. This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students' self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential. Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.

ChatGPT's advanced text generation capability has significant potential for the development of innovative and effective pedagogical strategies in mathematics teaching. The integration of ChatGPT into the mathematical story-focused 5E lesson plan preparation process is a concrete reflection of this potential. The success of this integration depends primarily on how mathematics teachers and pre-service teachers interact with and perceive ChatGPT. Therefore, the purpose of this study is to examine the interactions of three mathematics teachers and three pre-service teachers with ChatGPT during the process of preparing mathematical story-focused 5E lesson plans. The research aims to evaluate the qualities of the prompts created by the participants in this process, the benefits provided by ChatGPT, and the participants' metaphorical perceptions of ChatGPT. Open-ended questionnaires and focus group interviews were conducted, and participants' prompt archives were also analyzed. The data analysis incorporated a meticulously conducted inter-rater reliability assessment between a human expert and ChatGPT. The results revealed that the participants used ChatGPT differently, with particularly large variations in the number of prompts. Some participants created a large number of prompts, while others worked with much fewer prompts, but it was observed that creating a large number of prompts did not always translate into benefits. Factors like originality, time management, self-efficacy, and ChatGPT's mathematical performance were identified as limiting factors. Perceptions of ChatGPT varied, with some participants viewing it as an assistant and others associating it with cheating. Based on these findings, recommendations for effectively using ChatGPT are provided.

The AI-powered tools are transforming instructional methods and teaching strategies across the world. As conventional methods of instruction no longer align with the preferences of today's tech-savvy young generation, AI-powered tools are increasingly incorporated to facilitate learning and instruction. While multiple studies have examined various aspects of AI integration, there is a lack of evidence on the comparative effectiveness of AI-generated and instructor-prepared lesson plans. Therefore, this study examines the comparative efficacy of AI-generated and instructor-prepared lesson plans on students' performance and engagement. Utilizing a quasi-experimental design, the experimental group (Group A) used AI-generated lesson plans, while the control group (Group B) received instructor-prepared lesson plans. While a pre-test was administered to establish a baseline, a post-test and a delayed post-test were used to examine the impact on students’ performance. The findings revealed that AI-generated lesson plans were more effective and had a more positive impact on students' performance than instructor-prepared lesson plans. The study suggests how AI could enhance language instruction in EFL classrooms.