Teacher Professional Development with A.I.

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	==[https://ocul-bu.primo.exlibrisgroup.com/permalink/01OCUL_BU/p5aakr/cdi_doaj_primary_oai_doaj_org_article_93a83cbb1538431aa6b6f4d06d768293 Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning]==		==[https://ocul-bu.primo.exlibrisgroup.com/permalink/01OCUL_BU/p5aakr/cdi_doaj_primary_oai_doaj_org_article_93a83cbb1538431aa6b6f4d06d768293 Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning]==
-	Wei, L. (2023). Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14.	+	Wei, L. (2023). Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. ''Frontiers in Psychology, 14''.
	https://doi.org/10.3389/fpsyg.2023.1261955.		https://doi.org/10.3389/fpsyg.2023.1261955.
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	[[User:Lw19qc|Lw19qc]] 10:30, 4 December 2023 (EST)		[[User:Lw19qc|Lw19qc]] 10:30, 4 December 2023 (EST)
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		+	==[https://ocul-bu.primo.exlibrisgroup.com/permalink/01OCUL_BU/p5aakr/cdi_unpaywall_primary_10_1111_bjet_13232 Teachers trust in AI-powered educational technology and a professional development program to improve it]==
		+	Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI‐powered educational technology and a professional development program to improve it. ''British Journal of Educational Technology'', 53(4), 914–931.
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		+	https://doi.org/10.1111/bjet.13232
		+
		+	D.O.I: 10.1111/bjet.13232
		+
		+	===Context===
		+	The article addresses the critical topic of teachers’ attitudes and perceptions, which adds to the continuing conversation about adopting AI-EdTech in K–12 education. The study aligns with broader issues in educational technology research by examining the effect of a Professional Development Program (PDP) on teachers’ faith in AI-EdTech. The mentioned literature highlights the importance of human variables, such as trust, in the practical application of technology in various areas, particularly transportation and healthcare. This viewpoint is expanded to the field of AI-EdTech in the study, which emphasizes the difficulties brought about by teachers’ misunderstandings and lack of knowledge. The study’s design, influenced by Vereschak et al. (2021) recommendations, makes a methodological contribution by establishing an experimental environment that accurately depicts trust as an attitude in precarious circumstances. The emphasis on unfavourable elements that undermine trust, like ignorance and misunderstandings, fits with the larger research goal of removing obstacles to adopting AI-EdTech. Moreover, the suggestions provided for AI-EdTech developers and PDP designers offer helpful information. The subtle nature of trust-building is addressed by the focus on specific educational goals, transparency in AI processes, and acquainting teachers with required accuracy levels. These suggestions offer helpful principles for creating PDPs that work well and may be used in many educational settings.
		+
		+	===Overview===
		+	The authors aim to investigate teachers’ perceptions and attitudes towards AI-EdTech, explicitly focusing on the potential changes during a PDP and their impact on trust. Three research questions guide the study: (1) To what extent does teachers’ knowledge about AI-powered assessment change throughout the PDP? (2) To what extent do teachers’ perceptions and attitudes towards human and AI-powered assessment change throughout the PDP? (3) Do these changes reflect teachers’ trust and willingness to adopt AI-EdTech? The study uses a qualitative methodology in an eight-week professional development program for in-service high school biology teachers. An NLP and AI-powered assessment tool called AI-Grader is introduced in the intervention to evaluate biology students’ fabricated responses. The PDP’s design makes teachers vulnerable by highlighting the uncertainty surrounding significant results and setting up reasonable expectations for AI-Grader early. One-on-one interviews, three group sessions, and individual assignments are all part of the PDP format. The authors provide procedural knowledge and include a creative exercise, “The Masked Rater,” to address misconceptions about AI-EdTech and teachers’ lack of knowledge. The PDP is split into two halves, with “The Masked Rater” as a turning point to affect educators’ perspectives. While discourse analysis focuses on finding categories and subcategories from teachers’ discourses, data collection entails recording and transcribing sessions.
		+
		+	===Strengths and Weaknesses===
		+	The article’s greatest strength lies in its meticulous design and execution of a PDP to enhance teachers’ trust in AI-EdTech. The study addresses teachers’ lack of confidence and negative attitudes towards AI-EdTech by strategically integrating vital elements into the PDP. Using AI-Grader as a concrete tool for automated formative assessment, coupled with a focus on a pedagogical task, adds practical relevance and helps bridge the gap between theory and application. The study’s commitment to real-world applicability is further strengthened by using actual participant data, creating a situation of vulnerability for teachers and aligning with the principles of trust development. One notable weakness is the self-selected population of teachers with presumed positive expectations towards AI-EdTech. While the study acknowledges this limitation, it raises concerns about the generalizability of the findings to a more diverse and potentially skeptical teacher population. Addressing this limitation through a more varied sample could enhance the study’s external validity.
		+
		+	===Assessment===
		+	In conclusion, this study provides valuable insights into addressing teachers’ trust and attitudes toward adopting AI-EdTech through a well-designed Professional Development Program (PDP). Focusing on a concrete pedagogical task, including actual participant data, and emphasizing AI-common procedures contribute to a positive teacher perception shift. Notably, the PDP addresses the lack of knowledge and misconceptions about AI-EdTech, fostering trust through transparency and procedural justice. The recommendations for PDP creators and AI-EdTech developers offer practical guidance for designing effective interventions.
		+
		+	[[User:Lw19qc|Lw19qc]] 10:45, 4 December 2023 (EST)

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Revision as of 11:45, 4 December 2023

Contents 1 Context Statement 2 Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning 2.1 Context 2.2 Overview 2.3 Research Design and Hypothesis 2.4 Strengths and Weaknesses 2.5 Assessment 3 Teachers trust in AI-powered educational technology and a professional development program to improve it 3.1 Context 3.2 Overview 3.3 Strengths and Weaknesses 3.4 Assessment

Context Statement

AI-enhanced teacher professional development is vital to education, addressing key challenges. It enables tailored learning experiences for diverse classrooms, promoting scalability by overcoming geographical barriers. It fosters interactive and collaborative learning in the classroom. Real-time feedback loops encourage reflective practice. Striking a balance between human-centric ideals and technological integration is crucial for fully realizing AI’s educational benefits. Educators and policymakers must navigate these ethical issues to ensure AI complements human skills rather than replaces them.

Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning

Wei, L. (2023). Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14.

https://doi.org/10.3389/fpsyg.2023.1261955.

D.O.I: 10.3389/fpsyg.2023.1261955

Context

Based on Vygotsky’s social constructivist theory, this study explores the transformative effects of AI-assisted language acquisition on English as a Foreign Language (EFL) learners. The study’s thorough mixed-methods methodology, which synthesizes quantitative and qualitative data to illuminate the complex implications of AI in language teaching, is its most vital point. Qualitative findings validate the beneficial impact of AI-driven training on several facets of language learning accomplishment, consistent with earlier research conducted by Xu et al., Zheng et al., Hsu et al., and Utami et al. This provides a solid framework for the analysis in the larger context of AI in EFL instruction. Vygotsky’s theory, mainly focusing on AI-facilitated collaborative activities, demonstrates a sophisticated comprehension of how AI might function as a catalyst for language learners’ internalization of abilities. The comparison of learners with and without AI, which highlights the quicker shift from other-regulation to self-regulation, adds to the conversation on how AI supports learners’ autonomy. The paper discusses the synergy between traditional teaching and AI help by emphasizing the student-centred nature of language learning and the tailored feedback provided by AI. In line with recent studies on technology-enhanced learning settings, this nuanced integration offers insights into the pedagogical processes that improve language learning efficacy. Moreover, the study links to more general issues in education by emphasizing AI’s contribution to L2 motivation and self-regulated learning. The results are consistent with previous research, highlighting AI’s capacity to develop flexible, encouraging, and exciting learning environments.

Overview

The study aims to fill a research gap by quantitatively examining the effects of AI-assisted language learning on EFL learners’ English achievement, L2 motivation, and self-regulated learning. Despite encouraging results in the literature, the impact of these particular elements has yet to receive enough attention. The main focus of the study topics is on how learners perceive the effects of AI and how AI-assisted training differs from non-AI-assisted instruction. Using a mixed-methods approach, the study included 60 individuals in mainland China who participated in a 10-week Duolingo intervention. Prioritizing ethical issues, various tools are utilized to collect data, such as the SRQ, L2 motivation ratings, and English accomplishment assessments. Semi-structured interviews are part of the qualitative phase. The thematic analysis emphasizes the practical implications for language classrooms by integrating qualitative and quantitative findings to offer a thorough understanding of the influence of AI-mediated training.

Research Design and Hypothesis

This mixed-methods research investigates the impact of AI-assisted language learning on English proficiency, L2 motivation, and self-regulation among EFL learners. Conducted at a Chinese mainland university, participants (n = 60) from two classes were randomly assigned to experimental and control groups. The control group experiences traditional language teaching, while the experimental group uses Duolingo for AI-mediated instruction. Admission criteria include undergraduate status, no prior AI-mediated language education, and no learning impairments. Ethical considerations prioritize participant privacy and informed consent. Although not explicitly stating formal hypotheses, the study implies an experimental hypothesis, anticipating significant improvements in language achievement, motivation, and self-regulation with AI assistance. The quantitative phase, employing mixed-design ANOVA, analyzes pre-test and post-test scores for the main effects of time and group and their interactions. Qualitative insights are gathered through thematic analysis of semi-structured interviews. This comprehensive approach enhances understanding of the research topics, exploring the nuanced impact of AI on language learning.

Strengths and Weaknesses

The article uses a mixed-methods approach, combining quantitative analysis through ANOVA tests with qualitative insights from semi-structured interviews. This comprehensive methodology provides a nuanced understanding of the impact of AI-mediated language instruction. The statistical analysis, including ANOVA tests and descriptive statistics, demonstrates a meticulous approach to data analysis. The attention to assumptions, such as normality and homogeneity of variance, adds rigour to the study. The article presents findings in a structured manner using tables, making it easy for readers to grasp the key results. Including effect sizes (n2) enhances the interpretability of the statistical outcomes. The qualitative phase, with thematic analysis of interviews, enriches the study by providing a deeper understanding of students’ experiences. This integration enhances the validity of the findings. The study addresses a pertinent issue in language education: the impact of AI on English learning. The results contribute valuable insights, emphasizing the potential benefits of AI-mediated instruction in enhancing language learning outcomes. A weakness of the article is that the study’s findings may not apply as well to other contexts because it focuses on Chinese EFL learners. The study would be strengthened by acknowledging this restriction and discussing possible outcome differences across different learner demographics.

Assessment

This article effectively portrays the transformative effects of AI-assisted language acquisition on English as a Foreign Language (EFL) learners. This study achieved its goal of answering their two research questions: (1) “Are there any significant differences between AI and non-AI-assisted language learning instruction in developing English learning achievement, L2 motivation, and self-regulated learning of EFL learners?” (2) “What are EFL learners’ perceptions of the effects of AI-assisted language learning on their learning achievement?” This article offers helpful insights into AI and how it can be positively used for teacher professional development, explicitly assisting EFL learners.

Lw19qc 10:30, 4 December 2023 (EST)

Teachers trust in AI-powered educational technology and a professional development program to improve it

Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI‐powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931.

https://doi.org/10.1111/bjet.13232

D.O.I: 10.1111/bjet.13232

Context

The article addresses the critical topic of teachers’ attitudes and perceptions, which adds to the continuing conversation about adopting AI-EdTech in K–12 education. The study aligns with broader issues in educational technology research by examining the effect of a Professional Development Program (PDP) on teachers’ faith in AI-EdTech. The mentioned literature highlights the importance of human variables, such as trust, in the practical application of technology in various areas, particularly transportation and healthcare. This viewpoint is expanded to the field of AI-EdTech in the study, which emphasizes the difficulties brought about by teachers’ misunderstandings and lack of knowledge. The study’s design, influenced by Vereschak et al. (2021) recommendations, makes a methodological contribution by establishing an experimental environment that accurately depicts trust as an attitude in precarious circumstances. The emphasis on unfavourable elements that undermine trust, like ignorance and misunderstandings, fits with the larger research goal of removing obstacles to adopting AI-EdTech. Moreover, the suggestions provided for AI-EdTech developers and PDP designers offer helpful information. The subtle nature of trust-building is addressed by the focus on specific educational goals, transparency in AI processes, and acquainting teachers with required accuracy levels. These suggestions offer helpful principles for creating PDPs that work well and may be used in many educational settings.

Overview

The authors aim to investigate teachers’ perceptions and attitudes towards AI-EdTech, explicitly focusing on the potential changes during a PDP and their impact on trust. Three research questions guide the study: (1) To what extent does teachers’ knowledge about AI-powered assessment change throughout the PDP? (2) To what extent do teachers’ perceptions and attitudes towards human and AI-powered assessment change throughout the PDP? (3) Do these changes reflect teachers’ trust and willingness to adopt AI-EdTech? The study uses a qualitative methodology in an eight-week professional development program for in-service high school biology teachers. An NLP and AI-powered assessment tool called AI-Grader is introduced in the intervention to evaluate biology students’ fabricated responses. The PDP’s design makes teachers vulnerable by highlighting the uncertainty surrounding significant results and setting up reasonable expectations for AI-Grader early. One-on-one interviews, three group sessions, and individual assignments are all part of the PDP format. The authors provide procedural knowledge and include a creative exercise, “The Masked Rater,” to address misconceptions about AI-EdTech and teachers’ lack of knowledge. The PDP is split into two halves, with “The Masked Rater” as a turning point to affect educators’ perspectives. While discourse analysis focuses on finding categories and subcategories from teachers’ discourses, data collection entails recording and transcribing sessions.

Strengths and Weaknesses

The article’s greatest strength lies in its meticulous design and execution of a PDP to enhance teachers’ trust in AI-EdTech. The study addresses teachers’ lack of confidence and negative attitudes towards AI-EdTech by strategically integrating vital elements into the PDP. Using AI-Grader as a concrete tool for automated formative assessment, coupled with a focus on a pedagogical task, adds practical relevance and helps bridge the gap between theory and application. The study’s commitment to real-world applicability is further strengthened by using actual participant data, creating a situation of vulnerability for teachers and aligning with the principles of trust development. One notable weakness is the self-selected population of teachers with presumed positive expectations towards AI-EdTech. While the study acknowledges this limitation, it raises concerns about the generalizability of the findings to a more diverse and potentially skeptical teacher population. Addressing this limitation through a more varied sample could enhance the study’s external validity.

Assessment

In conclusion, this study provides valuable insights into addressing teachers’ trust and attitudes toward adopting AI-EdTech through a well-designed Professional Development Program (PDP). Focusing on a concrete pedagogical task, including actual participant data, and emphasizing AI-common procedures contribute to a positive teacher perception shift. Notably, the PDP addresses the lack of knowledge and misconceptions about AI-EdTech, fostering trust through transparency and procedural justice. The recommendations for PDP creators and AI-EdTech developers offer practical guidance for designing effective interventions.

Lw19qc 10:45, 4 December 2023 (EST)