Mikael Alemu Gorsky’s Post

🔺 Strategy and research group Ithaka S+R just published a report "Generative AI and Postsecondary Instructional Practices" authored by Dylan Ruediger, Melissa Blankstein and Sage Love. The comprehensive 33-page report investigates the current state of GenAI adoption in US higher education.   I find the insights both illuminating and concerning, particularly the slow and uneven integration of these transformative tools.   ☝🏾 The report reveals that while 72% of faculty have experimented with AI tools, only 11% have successfully integrated them into teaching. Furthermore, older faculty members struggle with confidence in AI’s pedagogical value, limiting broader adoption.   🤟🏾 A particularly troubling statistic is that 64% of faculty believe AI enhances student learning, yet only 36% are prepared to incorporate it into their courses. Only 20% of institutions provide substantial support for AI adoption, such as workshops and training sessions.   🖖🏾 The report highlights that only 14% of faculty feel very confident in their ability to use AI tools effectively in their teaching. This lack of confidence is a major hurdle that needs to be overcome through robust professional development programs.   Moreover, there is a clear demand for resources. Faculty members need access to AI tools, training materials, and dedicated support to explore and implement AI in their teaching. Without these resources, the adoption of AI will remain limited and uneven.   🥸 A notable finding from the report details how faculty encourage or allow students to use generative AI in their courses. The data reveals:   4️⃣2️⃣% of faculty PROHIBIT students’ use of generative AI in their courses 3️⃣7️⃣% allow its use ONLY for brainstorming ideas. 2️⃣3️⃣% permit its use for drafting and editing written assignments. 2️⃣3️⃣% encourage creating outlines with AI assistance. 2️⃣1️⃣% support using AI as a study guide 1️⃣7️⃣% approve of AI for conducting research, such as discovering and summarizing content or generating research questions 1️⃣2️⃣% (sic!) enable students to create images, music, or visualizations using AI 7️⃣% incorporate AI in language instruction It is very hard to believe but only 6️⃣% allow AI for writing code   This reluctance to embrace AI is a major obstacle that needs addressing. It suggests a need for more comprehensive education on the potential benefits and ethical use of AI in academic settings.   To move forward, institutions must adopt a proactive approach. This includes investing in professional development, creating supportive infrastructures, and fostering a culture of innovation. Collaboration between educators, AI experts, and policymakers is essential to develop comprehensive strategies for AI integration.   #GenerativeAI #HigherEducation #EdTech #AIinEducation #FutureOfLearning

Call for Papers: "𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐬𝐬𝐞𝐬𝐬𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐀𝐈: 𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬, 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡, 𝐚𝐧𝐝 𝐫𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬" - Frontiers in Communication For details: https://lnkd.in/dSV2Ecb8 Manuscript Summary Submission Deadline 31 January 2025 Manuscript Submission Deadline 28 March 2025 By the end of 2022, generative AI technologies based on large language models had become easily accessible and increasingly widespread. As disruptive technologies, their full impact and range of applications are still unfolding. This Research Topic offers a platform for researchers and educators to reflect on the impact of these technological shifts on classroom culture and to share pedagogical experiments that enhance teaching and assessing practices. The editorial team has chosen the ‘short-paper’ format to capture fresh ideas, conversations, and pedagogical experimentations, and to allow these to be easily shared with a broader audience of researchers and teachers alike. This multidisciplinary project welcomes contributions from teachers and researchers across a variety of fields (e.g., communication, education, computer science, economics, mathematics, and biology). We are particularly interested in practices with cross-disciplinary relevance that can shape classroom cultures at the tertiary level. The generative AI tools considered in the submissions must be available in a free or free-tier version, or commonly available as part of university-wide enterprise licenses, or similar. Submissions may focus on, but are not limited to, themes such as the following: • effective practices and/or lessons learned in integrating generative AI tools in classroom settings • successful strategies to support teaching, learning, and assessment with generative AI tools • real-time student feedback through AI-driven feedback mechanisms • critical discussions on academic integrity, critical thinking, authorship, and other ethical concerns related to generative AI in classroom teaching, assessment, and curriculum design • advances in instructional communication and/or pedagogical theories in the light of the integration of generative AI tools in classroom settings • student engagement, participation, and collaborative learning in the AI-mediated classroom • educational materials, instructional sources, and teacher professional development in the AI-mediated classroom • development of necessary knowledge and skills (e.g., digital literacy and critical generative AI literacy) for learning in the generative AI era • critical discussions of the challenges of generative AI to classroom culture (e.g., digital divide, power dynamics, and intercultural inequalities) • sustainability of generative AI tools in classroom settings.

Three Use Cases for AI in the Classroom The rapid advancements in large language models (LLMs) are prompting me to rethink my approach to teaching and learning. As I adapt, I’ve identified three distinct roles for AI in my classroom. 1. Course Design Before the class even begins, AI can assist in course design. While I often find AI-generated course structures to be uninspired, experimenting with supplying the LLM my own writing on the content topic has yielded better results. Currently, I use AI suggestions as a starting point, refining them to create a more engaging and personalized course structure. 2. Creating Quizzes and Assignments AI excels in handling the otherwise tedious tasks of creating quizzes and assignments. By feeding the AI examples of my related content, it generates assignments and useful grading rubrics tailored to my teaching style. This offloads some of the least desirable tasks, allowing me to focus on more critical aspects of teaching. In the fall, I plan to experiment with a third-party grading system. Meanwhile, I've been using AI to build my own grading assistant. By providing the AI with my content and grading rubrics, it can perform an initial review of anonymized papers. This has proven helpful in suggesting areas where I can offer better guidance to students. While this process is currently tedious and time-consuming, third-party providers are making significant strides in AI-facilitated grading. This technology can offer faster, more efficient grading, allowing for multiple rounds of feedback before final submission. This is a win-win, benefiting both students and instructors. 3. One-on-One Student Tutoring The most exciting application of AI is in one-on-one student tutoring. Early experiments were problematic, with AI getting easily confused by already confused students. However, as LLMs improve, so does their ability to provide accurate assistance. In the fall, I plan to provide students with prompts and instructions to direct the AI to related course content. As students work through the material, the AI can offer on-the-spot clarifications. Research consistently shows that one-on-one instruction is superior to lectures, as it addresses individual learning paces and knowledge gaps. Virtual tutors, especially when paired with a flipped classroom model, can eliminate the dilemma of either slowing down for less-prepared students or pressing ahead for those better prepared, leading to higher quality and more uniform outcomes.

🤖 MIT's Interesting Papers on Generative AI in Education 🎓 Massachusetts Institute of Technology (MIT) has just published a fascinating series of papers exploring the impact of generative AI on various industries, with a particular focus on education. These papers delve into the implications for society, human interaction, and more. They're easily accessible in various formats, including in audio format. Given I'm an educator, of great interest were the papers related to education. Here's a brief summary of the three education papers: 1️⃣ "When Disruptive Innovations Drive Educational Transformation" by Per Urlaub & Eva Dessein https://lnkd.in/gG7fw79d This paper compares the historical skepticism towards literacy, calculators, and Google Translate with the current debates around AI tools like ChatGPT in education. The authors argue that while new technologies often face resistance, they can enhance learning when thoughtfully integrated. 2️⃣ "Generative AI and K-12 Education: An MIT Perspective" by Eric Klopfer, Justin Reich, Hal Abelson & Cynthia Breazeal https://lnkd.in/gtYqRYNK This paper discusses the mixed reaction from educators to ChatGPT in schools and makes the case for a balanced, experimental approach to integrate AI into teaching. The authors provide a roadmap and list of questions to guide a school's discussion of adopting AI. 3️⃣ "Generative AI and Creative Learning: Concerns, Opportunities, and Choices" by Mitch Resnick https://lnkd.in/gCafFPVB This paper discusses the integration of generative AI in education, highlighting the need for a shift from traditional instruction-focused approaches to project-based, creative learning. The author emphasizes the importance of developing children's creativity, curiosity, and collaborative skills in a rapidly changing world. These papers offer invaluable insights into the future of education in the age of generative AI. I highly recommend reading them! You can find a link to all the papers here: https://lnkd.in/g-H3WBis

AI Tutor Pro (https://lnkd.in/eE3gMu2f) AI Tutor Pro is an innovative educational tool that leverages artificial intelligence to provide personalized tutoring experiences for students across various subjects and grade levels. This comprehensive platform aims to revolutionize the way students learn by offering tailored instruction, adaptive assessments, and real-time feedback. At the core of AI Tutor Pro is its advanced natural language processing capabilities, which allow it to understand and respond to students' questions in a conversational manner. This creates a more engaging and interactive learning environment compared to traditional textbooks or video lectures. The AI can explain complex concepts, break down problems into manageable steps, and provide examples that resonate with each student's unique learning style. One of the key features of AI Tutor Pro is its ability to assess a student's knowledge and skills continuously. As students interact with the platform, the AI analyzes their responses and performance to identify strengths and weaknesses. This data is then used to dynamically adjust the difficulty and focus of subsequent lessons, ensuring that each student is challenged appropriately and receives targeted support in areas where they struggle. The platform covers a wide range of subjects, including mathematics, science, language arts, and social studies, making it a versatile tool for students of all ages and academic levels. Additionally, AI Tutor Pro offers test preparation modules for standardized exams, helping students build confidence and improve their scores through targeted practice and strategy coaching. Parents and teachers can benefit from AI Tutor Pro's comprehensive reporting features, which provide insights into student progress, learning patterns, and areas requiring additional attention. This data-driven approach enables more effective collaboration between educators, parents, and students to support academic growth. With its 4.8/5 rating on Aixploria, AI Tutor Pro has garnered praise for its effectiveness and user-friendly interface. As education continues to evolve in the digital age, tools like AI Tutor Pro are paving the way for more personalized, accessible, and efficient learning experiences that cater to the individual needs of each student.

Stanford Researchers Propose ‘POSR’: A Unique AI Framework for Analyzing Educational Conversations Using Joint Segmentation and Retrieval Effective lesson structuring remains a critical challenge in educational settings, particularly when conversations and tutoring sessions need to address predefined topics or worksheet problems. Educators face the complex task of optimally allocating time across different problems while accommodating diverse student learning needs. This challenge is especially pronounced for novice teachers and those managing large student groups, who frequently struggle with time management and lesson organization. While evidence-based insights into lesson structuring could provide valuable feedback to educators, tutoring platforms, and curriculum developers, obtaining such insights at scale presents significant difficulties. The analysis of conversation structure around reference materials involves two distinct natural language processing challenges: discourse segmentation and information retrieval, each presenting unique complexities when applied to educational conversations where teaching approaches vary based on student needs. Previous approaches to conversation analysis have primarily focused on discourse segmentation as a preprocessing step for retrieval or summarization tasks. Traditional methods segment conversations based on various criteria like speech acts, topics, or conversation stages, depending on the domain. When applied to educational contexts, specifically for problem-oriented segments in mathematics discussions, these conventional approaches face significant limitations. Standard segmentation methods operate under the assumption that conversations follow predictable patterns and structures, which proves inadequate for educational conversations that are inherently diverse and adaptable. Also, mathematical information retrieval presents unique challenges due to the complexity of representing mathematical expressions in their proper context. The distinctive nature of mathematical discourse, combined with the variable structure of educational conversations, has highlighted the inadequacy of existing approaches in effectively analyzing and retrieving problem-oriented segments from mathematical tutoring sessions. Researchers from Stanford University introduced the Problem-Oriented Segmentation and Retrieval (POSR) framework, a unique approach that simultaneously handles conversation segmentation and links these segments to corresponding reference materials. This integrated approach distinguishes itself from traditional methods by utilizing known reference topics to guide both segmentation and retrieval processes, particularly in educational contexts. The framework’s effectiveness is demonstrated through LessonLink, a comprehensive dataset designed to analyze mathematical tutoring sessions. LessonLink encompasses 3,500 segments drawn from real-world tutoring conversations, covering 116 SAT® math problems...

Reading this Wired article got me thinking about the ways in which Flipped Learning (where students learn/get introduced to content at home/on their own before practicing in class with guidance and collaboration) can: 1) avoid issues with AI doing the learning for students 2) still create space for students to develop AI literacies 3) ensure students are learning content-based and SEL-based skills necessary for success. What might this look like, and what might this require? Here are my thoughts: 📝 Flipped Learning allows students to engage with material on their own terms, and creates space for them to grapple with it in their own styles. This necessitates teaching overtly into different ways of learning content, and provides space for students to use AI to help craft different schedules or suggestions for students as they develop their unique academic habits. 🌀 Flipped Learning emphasizes process over product early in the learning cycle, which necessitates lessons and materials that review various approaches rather than guide students to pre-determined outcomes. Students need literacy not only in learning different approaches to, but in creating their own processes to completing tasks successfully. AI can help by synthesizing some of the approaches and providing alternatives for students to try on their own. 👥 Flipped Learning creates plenty of space for personalization/independent learning, which means we also need to ensure we're creating FL experiences that push students to practice learning (not just creating) in community. AI can help offer students different ways to communicate (i.e. providing prompts to engage in conversations) or help draft group norms for students to rely on as they collaborate. 🛑 A reminder that generative AI models are constantly extracting users' data for parent companies to exploit for their own profit, and that generative AI requires huge amounts of natural resources to function. So within the hype cycle of AI in schools, follow calls from thinkers like Neil Selwyn to implement new EdTech with a critical lens, and foster that criticality in your students as well. There are tons of great ways we can encourage kids to develop AI literacies without undermining their opportunities to grapple with content, and without shaming them for using the newest tool to do what we all did (shoutout to the Cliffs Notes 'major themes' section for all the essay topic ideas!). What are your thoughts?

What happens when smart big tech gets into the education "market"? Efforts on making learning easier and more enjoyable. But is that always what we need? OpenAI’s “A Student’s Guide to Writing with ChatGPT” offers practical tips for using AI in academic writing, helping students brainstorm, draft, and revise more efficiently. The guide does make writing more accessible, but it reflects a systems-thinking approach that prioritizes ease and productivity over deeper learning needs. Some key issues that are overlooked ... 1. Structured human-AI workflow 2. Foundational writing skills 3. Social learning Learning is a complex process that requires emotional and social contexts, structured workflows, and cognitive effort. While OpenAI’s guide is a useful step forward, educators and students need to adopt a more holistic approach to take advantage of the benefits of AI ... and put in the effort to develop the skills of critical thinking and creativity. Many thanks to Dave Nelson for his recent insightful post on Marc Watkins Rhetorica Substack. It got me thinking more deeply about OpenAI’s writing guide and how education gets increasingly translated into and sometimes distorted by corporate functionality. This is worth minding as Big tech are increasingly monopolizing politics, society, and education--not to mention our personal and professional lives. The power asymmetries are getting bigger, as Harun Serpil recently reminded me. Nick Potkalitsky, PhD, Scott Sommers, PhD, Jason Slimon,

Smart Education: How AI is Shaping the Future of Learning Artificial Intelligence is revolutionizing various sectors, and education is no exception. The integration of AI into educational systems, often referred to as “smart education,” promises to transform how students learn and how educators teach. AI-powered tools and applications can provide instant feedback, helping students to identify and rectify mistakes immediately. This is particularly beneficial in subjects like mathematics and language learning, where timely corrections are crucial for mastery. For educators, AI offers powerful tools for assessing student performance and identifying areas where students struggle. This data-driven approach enables teachers to intervene early and provide targeted support, improving overall learning outcomes. AI is also making education more accessible. Language translation tools powered by AI can break down language barriers, allowing students from different linguistic backgrounds to access educational materials in their native languages. Similarly, AI can assist students with disabilities through speech recognition, text-to-speech, and other assistive technologies, fostering a more inclusive learning environment. Furthermore, AI can automate administrative tasks, giving educators more time to focus on teaching and student interaction. Tasks such as grading, scheduling, and even monitoring student progress can be handled efficiently by AI systems, reducing the administrative burden on teachers and allowing them to dedicate more time to instructional activities. However, the integration of AI in education is not without challenges. Concerns about data privacy, the need for substantial investment in technology infrastructure, and the potential for increased dependency on technology are significant issues that need to be addressed. In conclusion, AI has the potential to significantly enhance the educational experience by making learning more personalized, accessible, and efficient. As AI continues to evolve, its role in shaping the future of education will likely expand, offering innovative solutions to traditional educational challenges. The key to success lies in thoughtful implementation and addressing the associated challenges to ensure that the benefits of AI in education are fully realized.

Nagashree English School Embraces Generative AI with SIPSNITYA App Channarayapatna, June 21, 2024 - In an exciting development, Nagashree English School has taken a significant step towards enhancing the educational experience by integrating generative AI into their daily operations. Today, Mr. Rajath from the SIPSNITYA team conducted a comprehensive training session for all teachers and accounting staff. The training focused on equipping the staff with the skills to make the best use of the SIPSNITYA app, leveraging its generative AI capabilities to improve engagement and efficiency within the school. The implementation of this advanced technology is expected to bring numerous benefits, including: Enhanced Teaching Methods: Teachers can now utilize AI-generated content and tools to create more dynamic and personalized learning experiences for students. Improved Administrative Efficiency: The accounting staff will be able to streamline processes, reducing paperwork and allowing more time for strategic planning and support. Better Student Engagement: With AI-driven insights, teachers can better understand student needs and tailor their approaches to foster a more engaging and effective learning environment. Mr. Rajath's training session was both comprehensive and interactive, ensuring that all participants felt confident in using the new tools. The staff expressed enthusiasm about the potential improvements this integration would bring to their workflow and student outcomes. Nagashree English School is committed to staying at the forefront of educational innovation. By embracing generative AI, the school aims to provide a cutting-edge learning environment that prepares students for the future.