The vision of a universal AI tutor has remained elusive, despite decades of effort. Could LLMs be the game-changer? We overview novel issues arising from developing a nationwide AI tutor. We highlight the practical questions that point to specific gaps in our scientific understanding of the learning process.

As the global reach of online higher education continues to grow, universities are increasingly accommodating students from diverse cultural backgrounds (Tereshko et al., 2024). This can present a number of challenges including linguistic barriers (Ullah et al., 2021), cultural differences in learning style (Omidvar & Tan, 2012), cultural sensitivity in course design (Nguyen, 2022) and perceived isolation when students feel their perspectives or experiences are not reflected or valued in the learning environment (Hansen-Brown et al., 2022). Ensuring active engagement and reasonable learning outcomes in such a environments requires distance educational systems that are not only adaptive but also culturally resonant (Dalle et al., 2024). Both embodied and virtual AI-Agents have great potential in this regard as they can facilitate personalized learning and adapt their interactions and content delivery to align with students' cultural context. In addition, Generative AI (GAI), such as, Large Language Models (LLMs) can amplify the potential for these culturally aware AI agents to address educational challenges due to their advanced capacity for understanding and generating contextually relevant content (Wang et al., 2024). This chapter reviews existing research and suggests the usage of culturally aware AI-Agents, powered by GAI, to foster engagement and improve learning outcomes in culturally diverse online higher education environments.

While recent studies have examined the leaning impact of large language model (LLM) in educational contexts, the affective dynamics of LLM-mediated tutoring remain insufficiently understood. This work introduces the first ensemble-LLM framework for large-scale affect sensing in tutoring dialogues, advancing the conversation on responsible pathways for integrating generative AI into education by attending to learners' evolving affective states. To achieve this, we analyzed two semesters' worth of 16,986 conversational turns exchanged between PyTutor, an LLM-powered AI tutor, and 261 undergraduate learners across three U.S. institutions. To investigate learners' emotional experiences, we generate zero-shot affect annotations from three frontier LLMs (Gemini, GPT-4o, Claude), including scalar ratings of valence, arousal, and learning-helpfulness, along with free-text emotion labels. These estimates are fused through rank-weighted intra-model pooling and plurality consensus across models to produce robust emotion profiles. Our analysis shows that during interaction with the AI tutor, students typically report mildly positive affect and moderate arousal. Yet learning is not uniformly smooth: confusion and curiosity are frequent companions to problem solving, and frustration, while less common, still surfaces in ways that can derail progress. Emotional states are short-lived--positive moments last slightly longer than neutral or negative ones, but they are fragile and easily disrupted. Encouragingly, negative emotions often resolve quickly, sometimes rebounding directly into positive states. Neutral moments frequently act as turning points, more often steering students upward than downward, suggesting opportunities for tutors to intervene at precisely these junctures.

Artificial intelligence (AI) is rapidly reshaping educational practice, challenging long held assumptions about teaching and learning. This article integrates conceptual perspectives from recent books (Genesis by Eric Schmidt, Henry Kissinger and Craig Mundie, CoIntelligence by Ethan Mollick, and The Inevitable by Kevin Kelly) with empirical insights from popular AI podcasts and Anthropic public releases. We examine seven key domains: emotional support, creativity, contextual understanding, student engagement, problem solving, ethics and morality, and collaboration. For each domain, we explore AI capabilities, opportunities for transformative change, and emerging best practices, drawing equally from theoretical analysis and real world observations. Overall, we find that AI, when used thoughtfully, can complement and enhance human educators in fostering richer learning experiences across cognitive, social, and emotional dimensions. We emphasize an optimistic yet responsible outlook: educators and students should actively shape AI integration to amplify human potential in creativity, ethical reasoning, collaboration, and beyond, while maintaining a focus on human centric values.

Large Language Models (LLMs) are increasingly employed as AI tutors due to their scalability and potential for personalized instruction. However, off-the-shelf LLMs often underperform in educational settings: they frequently reveal answers too readily, fail to adapt their responses to student uncertainty, and remain vulnerable to emotionally manipulative prompts. To address these challenges, we introduce CoDAE, a framework that adapts LLMs for educational use through Chain-of-Thought (CoT) data augmentation. We collect real-world dialogues between students and a ChatGPT-based tutor and enrich them using CoT prompting to promote step-by-step reasoning and pedagogically aligned guidance. Furthermore, we design targeted dialogue cases to explicitly mitigate three key limitations: over-compliance, low response adaptivity, and threat vulnerability. We fine-tune four open-source LLMs on different variants of the augmented datasets and evaluate them in simulated educational scenarios using both automatic metrics and LLM-as-a-judge assessments. Our results show that models fine-tuned with CoDAE deliver more pedagogically appropriate guidance, better support reasoning processes, and effectively resist premature answer disclosure.

Simon Suh Department of Technology and Society Stony Brook University [Abstract] Driven by the global shift towards online learning prompted by the COVID-19 pandemic, Artificial Intelligence (AI) has emerged as a pivotal player in the field of education. Intelligent Tutoring Systems (ITS) offer a new method of personalized teaching, replacing the limitations of traditional teaching methods. However, concerns arise about the ability of AI tutors to address skill development and engagement during the learning process. In this paper, I will conduct a quasi-experiment with paired-sample t-test on 34 students pre-and post-use of AI tutors in language learning platforms like Santa and Duolingo to examine the relationship between students' engagement, academic performance, and students' satisfaction during a personalized language learning experience. Keywords: Artificial Intelligence; Academic Performance; ITS Education; Student Engagement; Language Learning; Personalized Learning; Student Satisfaction 1. Introduction The educational landscape is undergoing a transformative shift with the integration of Artificial Intelligence (AI). Technologies like Intelligent Tutoring Systems (ITS), specifically designed to provide individualized instruction and feedback to learners (Sedlmeier, 2002), play a crucial role in this transformation, steering the educational landscape towards the Application of Artificial Intelligence in Education (AIEd) (Thomas et al., 2023). As the accessibility and diversity of AI technologies increase, this holds a significant potential to personalize learning experiences and unlock the educational potential of each student by fostering a more efficient and effective learning process (Rane, 2023).

Alpha School co-founder Mackenzie Price and a junior at the school Elle Kristine join'Fox & Friends' to discuss the benefits of incorporating artificial intelligence into the classroom. At a time when many American students are struggling to keep up, a private school in Texas is doing more with less, much less. At Alpha School, students spend just two hours a day in class, guided by an Artificial Intelligence (AI) tutor. But results are impressive: students are testing in the top 1 to 2% nationally. "We use an AI tutor and adaptive apps to provide a completely personalized learning experience," said Alpha co-founder MacKenzie Price during an interview on Fox & Friends.

Alpha School co-founder Mackenzie Price and a junior at the school, Elle Kristine, join'Fox & Friends' to discuss the benefits of incorporating artificial intelligence into the classroom. A Texas private school is seeing student test scores soar to new heights following the implementation of an artificial intelligence (AI) "tutor." At Alpha School in Austin, Texas, students are placed in the classroom for two hours a day with an AI assistant, using the rest of the day to focus on skills like public speaking, financial literacy, and teamwork. "We use an AI tutor and adaptive apps to provide a completely personalized learning experience for all of our students, and as a result our students are learning faster, they're learning way better. In fact, our classes are in the top 2% in the country," Alpha School co-founder Mackenzie Price told "Fox & Friends." Will A.I. make schools'obsolete,' or does it present a new'opportunity' for the education system?

In recent years, Large Language Models (LLMs) rapidly gained popularity across all parts of society, including education. After initial skepticism and bans, many schools have chosen to embrace this new technology by integrating it into their curricula in the form of virtual tutors and teaching assistants. However, neither the companies developing this technology nor the public institutions involved in its implementation have set up a formal system to collect feedback from the stakeholders impacted by them. In this paper, we argue that understanding the perceptions of those directly affected by LLMS in the classroom, such as students and teachers, as well as those indirectly impacted, like parents and school staff, is essential for ensuring responsible use of AI in this critical domain. Our contributions are two-fold. First, we present results of a literature review focusing on the perceptions of LLM-based chatbots in education. We highlight important gaps in the literature, such as the exclusion of key educational agents (e.g., parents or school administrators) when analyzing the role of stakeholders, and the frequent omission of the learning contexts in which the AI systems are implemented. Thus, we present a taxonomy that organizes existing literature on stakeholder perceptions. Second, we propose the Contextualized Perceptions for the Adoption of Chatbots in Education (Co-PACE) framework, which can be used to systematically elicit perceptions and inform whether and how LLM-based chatbots should be designed, developed, and deployed in the classroom.

Today's generative AI systems are tuned to present information by default rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of \textit{pedagogical instruction following}, where training and evaluation examples include system-level instructions describing the specific pedagogy attributes present or desired in subsequent model turns. This framing avoids committing our models to any particular definition of pedagogy, and instead allows teachers or developers to specify desired model behavior. It also clears a path to improving Gemini models for learning -- by enabling the addition of our pedagogical data to post-training mixtures -- alongside their rapidly expanding set of capabilities. Both represent important changes from our initial tech report. We show how training with pedagogical instruction following produces a LearnLM model (available on Google AI Studio) that is preferred substantially by expert raters across a diverse set of learning scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.