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I chose to explore AI in education because I think it’s one of the most relevant and rapidly evolving areas today. As someone who is interested in both artificial intelligence and education as a career field post-graduation, this session's assignment is a great opportunity to dig deeper and gain more insight into how AI is transforming the learning experience for both teachers and students. In the Padlet wall, I’m planning to cover the following three specific AI applications in education: intelligent tutoring and adaptive learning systems (e.g. AutoTutor), automated assessment and feedback tools (e.g. Khanmigo), and AI-powered administrative or support tools like chatbots and lesson generators (e.g. Educhat). I’ll also explore the future trends (how much will we integrate it?) and ethical considerations (is it right to learn from AI? How does this affect educators?), along with the broader societal impacts of AI in education (its influence on equity, privacy, and the role of educators). This topic is especially significant to me because I’ve experienced the impact of AI in the classroom from both sides: as a student using AI-powered tools, and as an instructor integrating them into my lessons. Seeing how AI can enhance (and sometimes complicate) the learning process has made me want to better understand its full potential, as well as its risks. Through this project, I hope to learn more about how AI is shaping the future of education, and what role educators, learners, and technologists need to play in responsibly guiding its use.

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AutoTutor is an intelligent tutoring system that holds a natural language dialogue with students, guiding them through both problem solving and conceptual understanding. It uses computational linguistics techniques like latent semantic analysis, speech act classification, and script-based dialogue management to interpret student responses and decide how to respond [1]. The benefits include more personalized pacing, deeper understanding (especially in STEM topics), and scaling one‑on‑one tutoring to more learners. However, challenges include the limitation of domain coverage (only certain subjects), potential misinterpretation of student input, and opacity in how it “decides” what hint/prompt to give [2]. I find this example interesting because it approximates a human tutor’s adaptive support - if we can improve these systems, more students could have access to high-quality, individualized help from anywhere. As a tutor myself, I believe this is a great resource in addition to school.

[1] https://autotutor.org/history/

[2] https://link.springer.com/article/10.3758/BF03195563

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About AutoTutor - How it works

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AutoTutor comprehension strategies for adult learners

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Gradescope is an AI‑assisted grading tool used in many higher education settings, including at Stony Brook. It uses features like Answer Groups and AI Assistance to automatically cluster similar student answers (for fixed-format questions) and suggest groupings to speed up grading [3]. The benefits include reduced grading time, more consistent evaluation, and faster feedback loops for students [4]. But limitations include only working well for more structured or constrained question types (not very suited to open essays), potential errors with misgrouping that need human oversight, and sometimes dependency on rubrics or training data that reflect biases [5]. I chose this example because Gradescope is a practical, already‑adopted tool in classrooms that connects human judgment and automation and also highlights how AI can lighten teachers’ workload while still requiring human checks.

[3] https://guides.gradescope.com/hc/en-us/articles/24838908062093-AI-Assisted-Grading-and-Answer-Groups

[4] https://www.turnitin.com/products/gradescope/

[5] https://www.notieai.com/gradescope-review-2025-6-month-experience/

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Explanation of AI-assisted Grading and Answer Grouping in Gradescope

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Khanmigo is an AI-powered teaching assistant built on GPT-4 and developed by Khan Academy. It helps students by explaining problems step-by-step and assists teachers with lesson planning, feedback, and content generation. Using natural language processing and generative AI, it engages learners in “Socratic-style dialogue”, which means that it is guiding without giving direct answers [6]. Some benefits include personalized support, real-time help, and reduced prep time for teachers. However, it faces limitations like potential inaccuracies, overreliance by students, and data privacy concerns [7]. I find Khanmigo interesting because it merges AI tutoring with classroom support in a user-friendly, educator-backed tool, and gives students an alternative to other AI resources that may give them answers directly.

[6] https://vnfoundation.org/en/khanmigo-and-its-pioneering-role-in-the-ai-education-era/

[7] https://www.mamasmiles.com/khanmigo-review/

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An intro and explanations of the basics and foundation of Khanmigo by Sal Khan

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What is Khanmigo and how does it work | Introducing Khanmigo | Khanmigo for students | Khan Academy

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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AI in education is shifting from being a set of standalone tools to becoming an integrated, intelligent layer that supports and adapts to learners’ needs. One emerging trend is the rise of social generative AI (systems that engage in a two-way dialogue, build personalized knowledge maps for students, and serve as mentors instead of static responders) [8]. Another major development is the merging of adaptive learning and generative AI, where content, feedback, and problem sets are created almost immediately to match individual learner profiles. Additionally, schools are starting to emphasize AI literacy in their curriculums, teaching students not just how to use AI, but how to understand, question, and evaluate it critically.

However, serious ethical concerns follow innovation. One of the main issues is equity - if only well-funded schools can afford advanced AI tools, gaps that already exist in education would only get wider [9]. Bias and fairness are also risks, since AI models usually reflect and perpetuate systemic inequalities that are present in their training data [10]. Privacy and data governance are another concern, because sensitive student interaction data has to be handled securely and transparently. There's also the issue of academic integrity - generative AI blurs the line between student-authored and AI-assisted work, and teachers' skills may decrease if AI takes over too many instructional tasks, and opacity in decision-making might raise questions about accountability.

From a personal standpoint, I strongly believe that AI should augment, not replace, educators. It should empower both teachers and students, not override human judgment. Most importantly, students must learn to think critically about AI, and how to question its outputs and decide when to trust or reject them.

[8] https://www.tandfonline.com/doi/full/10.1080/23735082.2023.2261131

[9] https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/08/the-potential-impact-of-artificial-intelligence-on-equity-and-inclusion-in-education_0d7e9e00/15df715b-en.pdf

[10] https://pmc.ncbi.nlm.nih.gov/articles/PMC8455229/

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This assignment has helped me learn a lot about AI in education, including when and how it’s being used, the implications of its usage, and its impact directly in the classroom along with its direct effect on student learning as well as its potential to reshape teacher roles and future learning environments. The only challenge I would say I faced is finding non-biased sources, because a lot of the sources leaned pretty heavily in one direction or another (that we should integrate AI, or that we should ban it). Though it is improbable that any of my sources are completely free of bias, I made sure to look for pieces that were more research oriented and contemplative as opposed to argumentative. Personally, I think AI is making both a positive and negative impact in education, and it’s important that we learn how to use it properly to benefit from it. My research hasn’t heavily influenced my perspective on AI, but it has definitely increased my understanding of its use in education.

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The video cut off at 1:59, but the last point I wanted to make is that I am even more curious now about the role AI will play in the future of teaching and learning! Thank you for watching

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AI has already had a profound impact on education, and it’s clear that its influence will only continue to grow. One thing I keep coming back to is the question of educational equity. If access to advanced AI tools is limited to only wealthier schools or districts, it reinforces inequality and makes the educational divide worse [11]. But if we can find a way to make these tools accessible to everyone, AI could actually help close that gap - especially by offering personalized help where resources are limited. To achieve this, we must have thoughtful, inclusive implementation.

Employment in education is also shifting. AI is changing how teachers work (automating grading or lesson planning), allowing teachers to focus on mentorship and student relationships. However, this also means that teachers need new skills like AI oversight (understanding how AI works, how to monitor it, and how to use it effectively) and data literacy [12]. New roles will likely form too, like AI mentors/facilitators or digital ethics coaches, as human-AI collaboration becomes common in the classroom. Though this is exciting, it also feels like a big shift in what it means to teach.

Privacy is another growing concern. AI tools collect large amounts of behavioral data, including clicks, time spent on questions, even how attentive someone is [13]. If that data’s not protected or used ethically, it could be hacked or misused, seriously harming students’ trust in the system, and learning environments that are surveillance-based will form, risking further reduction in trust. There’s also the issue of cognitive offloading. If students rely too heavily on AI to write essays or solve problems, critical thinking and creativity may decline, over time resulting in shallower learning and a loss of essential skills.

Personally, I’m optimistic about what AI can bring, but also cautious in my optimism. There’s a fine line between using AI as a tool to support learning and letting it replace the human elements that make education meaningful. It must be developed and implemented with transparency, ethics, and inclusive intent. Otherwise, the risks could outweigh the rewards.

[11] https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons

[12] https://www.researchgate.net/publication/392410973_Empowering_Educators_Leveraging_AI_to_Revolutionize_Lesson_Planning

[13] https://edly.io/blog/artificial-intelligence-in-education-and-privacy-concerns/

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Graesser, A. C., & McNamara, D. S. (2010). AutoTutor and the learning sciences: A naturalistic dialogue approach. Behavior Research Methods, 42(1), 1–14. https://link.springer.com/article/10.3758/BF03195563
[Rationale: This article explains how AutoTutor functions as an intelligent tutoring system, providing insight into its underlying design and research foundations.]

AutoTutor.org. (n.d.). History of AutoTutor. https://autotutor.org/history/
[Rationale: The official AutoTutor site provides background on the system’s development, purpose, and evolution, which helped me understand its educational use.]

Gradescope. (2025). AI-assisted grading and answer groups. https://guides.gradescope.com/hc/en-us/articles/24838908062093-AI-Assisted-Grading-and-Answer-Groups
[Rationale: This resource explains how Gradescope’s AI grading features work in practice, showing how automation is implemented in real classrooms.]

Turnitin. (n.d.). Gradescope by Turnitin. https://www.turnitin.com/products/gradescope/
[Rationale: Provides details about Gradescope’s AI integration and benefits from an institutional technology provider’s perspective (TurnitIn). This was used more informationally, since it is biased.]

Notie AI. (2025). Gradescope review: 6-month experience. https://www.notieai.com/gradescope-review-2025-6-month-experience/
[Rationale: This source offers a practical, user-based review of Gradescope’s performance and limitations, which gives a really balanced real-world perspective.]

VN Foundation. (2024). Khanmigo and its pioneering role in the AI education era. https://vnfoundation.org/en/khanmigo-and-its-pioneering-role-in-the-ai-education-era/
[Rationale: This article describes Khanmigo’s role in shaping the modern classroom and its educational impact.]

Mama Smiles. (2024). Khanmigo review. https://www.mamasmiles.com/khanmigo-review/
[Rationale: Provides an accessible review of Khanmigo’s usability and challenges, which added helpful insight into both student and teacher perspectives.]

Huang, Y., & Li, M. (2023). Social generative AI and the future of intelligent tutoring systems. Journal of Educational Technology, 39(4), 202–215. https://www.tandfonline.com/doi/full/10.1080/23735082.2023.2261131
[Rationale: Discusses the integration of social generative AI into education, especially useful when I was analyzing Future Trends.]

Organisation for Economic Co-operation and Development (OECD). (2024). The potential impact of artificial intelligence on equity and inclusion in education. https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/08/the-potential-impact-of-artificial-intelligence-on-equity-and-inclusion-in-education_0d7e9e00/15df715b-en.pdf
[Rationale: Provides authoritative analysis of how AI affects equity and inclusion in global education systems.]

Vinuesa, R., et al. (2021). Bias and fairness in AI educational systems. Frontiers in Artificial Intelligence, 4, 1–12. https://pmc.ncbi.nlm.nih.gov/articles/PMC8455229/
[Rationale: Offers a peer-reviewed discussion on AI bias and fairness relevant to ethical considerations in education.]

University of Illinois Urbana-Champaign. (2024, October 24). AI in schools: Pros and cons. https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons
[Rationale: Discusses both benefits and risks of AI in education, used in the Societal Impact section.]

Kaur, A., & Sharma, P. (2024). Empowering educators: Leveraging AI to revolutionize lesson planning. ResearchGate. https://www.researchgate.net/publication/392410973_Empowering_Educators_Leveraging_AI_to_Revolutionize_Lesson_Planning
[Rationale: Explains how AI changes teachers’ work, supporting discussion on evolving employment roles.]

Edly. (2024). Artificial intelligence in education and privacy concerns. https://edly.io/blog/artificial-intelligence-in-education-and-privacy-concerns/
[Rationale: Focuses on privacy and ethical data use, complementing discussion of student data and surveillance.]