Introduction

"Why do I need to learn this? I'll never use it in real life." Few subjects provoke this perennial protest more than mathematics. Yet, beneath such skepticism lies an enduring and often underappreciated truth: mathematics cultivates a form of disciplined thinking that extends well beyond numbers and equations. It fosters cognitive habits that are essential for navigating complex situations, including logical structuring, pattern recognition, abstract modeling, and deductive reasoning, all of which form the basis for sound decision-making in fields such as finance, medicine, engineering, and public policy (Gravemeijer et al., 2017; Shimizu & Vithal, 2023). In an era increasingly defined by data-driven systems and algorithmic processes, the ability to think mathematically is no longer reserved for specialists; it has become a form of cultural and intellectual capital. Despite its value, however, mathematics remains one of the most formidable subjects for learners, frequently associated with high cognitive load, anxiety, and a fragile sense of self-efficacy (Rozgonjuk et al., 2020; Zakariya, 2022). In response to these challenges, educational systems worldwide have turned to instructional technologies as a strategic intervention. The integration of these tools aims to reduce the affective and cognitive friction that has long hindered mathematical achievement. Given the pedagogical benefits of technology-enhanced learning (Foshee et al., 2016; Na & Sung, 2025), such tools are increasingly used to mitigate learning barriers, support differentiated instruction, and provide real-time formative feedback. As these tools evolve in pedagogical adaptability, they are also beginning to converge with a new class of systems that extend beyond content delivery and into the orbit of intelligent interaction.

Artificial intelligence (AI) is one of the most consequential advancements in the current wave of educational technology innovation (Acut et al., 2025; Gantalao et al., 2025; Mangubat et al., 2025). Broadly defined, AI refers to the development of computational systems capable of performing tasks that typically require human intelligence, including pattern recognition, decision-making, natural language processing (NLP), and adaptive learning. In educational contexts, AI systems leverage probabilistic modeling, deep neural networks, and real-time data analytics to generate contextually appropriate responses and support personalized instruction (Izquierdo-Álvarez & Jimeno-Postigo, 2025). Unlike earlier generations of rule-based software, contemporary AI platforms are dynamic and responsive, capable of adjusting instructional content and feedback based on individual learner profiles. Their increasing adoption reflects a pedagogical shift toward scalable, adaptive, and dialogic models of teaching and learning (Xiao et al., 2025). According to Bozkurt et al. (2024), the integration of AI into education embeds assumptions, values, and cultural biases that can influence learning in both empowering and problematic ways. Yet amid these complexities, mathematics education stands out as a particularly fertile ground for inquiry. The inherent logic, structure, and abstraction of mathematics present a compelling match for AI's capacity to scaffold complex reasoning, personalize instruction, and engage learners in novel forms of mathematical thinking. Against this backdrop, this chapter seeks to address the question: How are AI technologies transforming the tools, practices, and pedagogical frameworks of mathematics education in ways that enhance learning while preserving disciplinary rigor?

Main Focus of the Chapter

AI is rapidly reshaping the landscape of education, and its integration into mathematics instruction is becoming increasingly prominent (e.g., Hwang & Tu, 2021; Mustafa, 2024). Despite the proliferation of research on AI in broader educational contexts, the field of mathematics remains comparatively underrepresented in the literature. This gap is particularly noteworthy given the inherently logical, structured, and problem-oriented nature of mathematics, which aligns well with the capabilities of AI systems. Bridging this gap is not only timely but essential, as mathematics continues to serve as a foundational discipline across STEM fields and requires innovative approaches to address persistent challenges related to engagement, personalization, assessment, and conceptual understanding (Engelbrecht & Borba, 2024). This chapter aims to critically examine the emerging role of AI in mathematics education by synthesizing current tools, identifying prevailing trends, and exploring transformative applications. It brings together a wide spectrum of AI-driven innovations, with particular attention to how these technologies are being deployed to support mathematical reasoning, improve equity in instruction, and promote deeper student engagement. In focusing exclusively on mathematics education, this chapter contributes a discipline-specific lens to a field often dominated by generalist perspectives. It highlights how AI can address unique pedagogical challenges in mathematics, such as the need for stepwise feedback, symbolic interpretation, and cognitive scaffolding. Moreover, it provides educators, researchers, and policymakers with a structured understanding of the current landscape and future directions of AI integration in mathematics instruction. Articulating both the possibilities and limitations of these technologies underscores the importance of evidence-based adoption that prioritizes learning outcomes and preserves the intellectual rigor of the discipline.

Methodology

Methodological Orientation

This chapter employed a narrative review supported by expert-informed synthesis to examine the integration of AI into mathematics education. A narrative review was deemed appropriate due to the emerging and interdisciplinary nature of the topic (Sukhera, 2022), which spans multiple domains including educational technology, mathematics pedagogy, and AI research. Narrative reviews are well-suited for exploring under-theorized areas by integrating diverse sources, uncovering conceptual patterns, and highlighting emergent practices (Greenhalgh et al., 2018; Sarkar & Bhatia, 2021). Unlike systematic reviews, this method permits thematic flexibility and accommodates both peer-reviewed scholarship and practice-based insights. To enrich the analysis, the review was grounded in the authors' combined expertise in AI-supported learning environments, classroom instruction, and instructional design. This expert-informed dimension added contextual depth to the interpretation of literature and facilitated the selection of representative tools and frameworks. Expert-informed reviews have been recognized as valuable in education research for surfacing tacit practitioner knowledge and shaping grounded, experience-based interpretations (Grant & Booth, 2009).

Conceptual Framework

The review was guided by a three-part conceptual framework that focuses on pedagogical functionality, learner interaction, and instructional innovation. Pedagogical functionality refers to how well an AI tool supports essential instructional practices such as scaffolding, differentiation, or formative assessment. Learner interaction concerns the degree to which students engage with AI tools in adaptive, personalized, or dialogic ways. Instructional innovation captures whether a tool enables novel modes of teaching and learning, especially those that promote conceptual understanding and higher-order reasoning. This framework draws on theoretical insights from prior work on AI in various fields of education (e.g., Bozkurt et al., 2024; Garcia et al., 2025), while tailoring the lens specifically to mathematics education. The structured and abstract nature of mathematics lends itself particularly well to AI-mediated instructional models that rely on formal rule-based reasoning and symbolic manipulation. As such, this framework facilitates not only the classification of AI tools but also a more nuanced understanding of how they intersect with the cognitive and representational demands unique to mathematics.

Selection of Tools and Examples

The selection of AI tools and examples for analysis in this chapter was guided by a set of conceptual and pedagogical criteria. Tools were included if they demonstrated clear relevance to mathematics education and aligned with at least one of the chapter's core dimensions: pedagogical functionality, learner interaction, or instructional innovation. Priority was given to tools that addressed persistent instructional challenges in mathematics, such as providing stepwise feedback, visualizing abstract concepts, supporting personalized pacing, or enhancing formative assessment practices. Additionally, selected tools were required to incorporate core AI capabilities, including but not limited to machine learning algorithms, NLP, probabilistic reasoning, or adaptive sequencing (Izquierdo-Álvarez & Jimeno-Postigo, 2025). The selection process was informed by the authors' professional engagement with AI-enhanced educational technologies, along with input from peers and practitioners in mathematics education. In some cases, tools were identified through ongoing involvement in research projects, classroom experimentation, or participation in academic networks focused on educational innovation. While the approach was not exhaustive or systematic, it aimed to ensure a representative breadth of instructional contexts, educational levels, and AI functionalities. Rather than emphasizing brand-specific features, the analysis focused on how the tools instantiated underlying pedagogical principles and leveraged AI to mediate mathematical learning in meaningful ways.

Literature Search and Source Selection

To complement the expert-informed tool selection, a structured literature review was conducted focusing on publications from 2020 to 2025. Scholarly databases such as ERIC, Scopus, and Web of Science were searched using combinations of keywords including and related to "artificial intelligence," and "mathematics education." Preference was given to peer-reviewed journal articles, empirical studies, and high-impact conference proceedings. In addition to academic sources, the review considered relevant grey literature including institutional white papers, education technology evaluations, and policy reports. Inclusion criteria prioritized sources with conceptual clarity, methodological rigor, and direct relevance to mathematics instruction. Foundational sources include Nguyen et al. (2023) on ethical implementation of AI in education, Liu et al. (2025) on AI-enabled learning environment for mathematical activities, and Hwang and Tu (2021) on bibliometric trends in AI-driven mathematics education research.

Results and Discussion

The integration of AI into mathematics education represents a rapidly evolving frontier with the potential to transform not only how students engage with mathematical content but also how educators design and deliver instruction across learning environments. This chapter is significant in that it synthesizes emerging insights from diverse fields to illuminate how intelligent systems can meaningfully support mathematics learning. Given the structured and cumulative nature of mathematics, adaptive technologies such as AI are uniquely positioned to address long-standing challenges related to conceptual misunderstanding, learner disengagement, and instructional differentiation. More broadly, the findings contribute to the wider field of education by demonstrating how AI can serve as a catalyst for pedagogical innovation, equity in learning access, and data-informed decision-making. This section presents the major themes (Figure 1) that emerged from the review, each of which illustrates how AI technologies are being leveraged to address challenges in mathematics education while advancing pedagogical practices. The discussion integrates illustrative examples of AI tools to highlight their instructional relevance, underlying mechanisms, and alignment with the conceptual framework introduced earlier.

Personalized Learning Systems

Personalized learning has become a cornerstone of modern educational reform (Maher, 2024), grounded in the principle that instruction should adapt to the needs, pace, and readiness of individual learners. In mathematics education, where conceptual progression is highly sequential and error propagation is common, adaptive support is particularly critical. Intelligent Tutoring Systems (ITS) operationalize this principle by delivering data-driven and individualized guidance based on student interactions, performance patterns, and inferred cognitive states. These systems utilize machine learning models to continuously update learner profiles and recalibrate instruction in real time, which offers scaffolding, revisits prerequisite concepts, or accelerates when mastery is demonstrated. MATHia (Figure 2), developed by Carnegie Learning, exemplifies this pedagogical paradigm by providing students with dynamically generated problem sets, step-by-step feedback, and tailored learning paths aligned with their evolving proficiency. Designed to mirror the decision-making process of a human tutor, this application employs cognitive modeling to assess conceptual mastery and personalize instruction with precision.

The pedagogical significance of AI-powered personalized learning systems lies in their ability to optimize both the pace and trajectory of mathematical instruction. Continuously assessing learner progress allow these systems implement adaptive sequencing that ensures students are neither overwhelmed by premature complexity nor disengaged by redundant review (Kabudi et al., 2021). This alignment with Vygotskian principles of the zone of proximal development creates a learning environment that is optimally challenging and cognitively supportive (Chaiarwut et al., 2025). Additionally, these platforms promote learner agency by tolerating students to navigate their learning paths while receiving contextualized feedback that builds metacognitive awareness and strategic competence. Moreover, ITS contribute to instructional equity by addressing variability in prior knowledge and learning pace. These conditions are often magnified in heterogeneous classrooms. They also offer instructors rich diagnostic data, enabling targeted interventions and more efficient resource allocation. In formative assessment contexts, ITS supports the identification of latent misconceptions and provides real-time corrective feedback that fosters durable learning. As personalized learning becomes increasingly central to mathematics instruction, AI-driven systems stand out not only for their scalability but also for their potential to deliver rigorous, individualized, and data-informed learning experiences that align with both curricular goals and cognitive science.

Automated Feedback and Assessment

Assessment remains a cornerstone of mathematics education, yet the process of evaluating student work (particularly for open-ended and multi-step problems) poses significant logistical and pedagogical challenges (Nortvedt & Buchholtz, 2018). Traditional grading is often labor-intensive, inconsistently applied, and limited in its ability to offer individualized feedback. AI has introduced a new class of automated assessment platforms that streamline the evaluation process while maintaining rigor and precision (Garcia et al., 2025; Xia et al., 2024). These platforms utilize a combination of NLP, image recognition, and pattern-matching algorithms to analyze student submissions, identify partial correctness, and generate customized feedback. Among the most widely adopted tools in this domain is Gradescope (Figure 3), an AI-assisted assessment platform designed to support the efficient grading of mathematical proofs, equations, and constructed responses. It enables educators to cluster similar answers, apply rubric-based grading at scale, and provide annotated feedback with a high degree of consistency. The system also supports handwritten submissions, which makes it especially useful in mathematics contexts where symbolic notation and diagrammatic representation are essential. By automating lower-order tasks and centralizing assessment data, this kind of tool enables instructors to monitor both individual and class-level learning trajectories with greater precision.

Building on these capabilities, AI-powered assessment platforms are fundamentally reshaping pedagogical practice in mathematics education. These systems analyze solution processes, recognize partial credit, and deliver feedback that is both immediate and instructional. Fundamentally, Sangwin and Köcher (2016) emphasized that the educational impact of automated assessment is in its ability to prompt reflection and facilitate knowledge reconstruction. Empirical evidence demonstrated that the strategic use of immediate feedback in automated homework systems led to higher levels of participation, improved performance, and enhanced metacognitive regulation (Gaona et al., 2018), particularly in large-scale mathematics courses where individualized support is often limited. Recent advances in AI, particularly through large language models (LLMs), have extended these affordances (Hasanah et al., 2025). Morris et al. (2024) reported that such models are now capable of evaluating constructed mathematical responses with a degree of interrater reliability comparable to that of trained human graders. This application makes it increasingly feasible to scale high-quality assessments that elicit student reasoning, without defaulting to multiple-choice or short-answer formats. As assessment moves toward competency-based, reasoning-centered paradigms, AI stands to play a central role in supporting equitable, transparent, and instructionally aligned evaluation.

NLP-Based Problem-Solving Tools

NLP, a branch of AI that enables computers to interpret, analyze, and generate human language, is increasingly influencing how students engage with mathematical tasks. Although NLP has traditionally been associated with applications such as sentiment analysis, chatbots, and language translation, it is now playing a transformative role in mathematics education by enabling the automated interpretation of word problems. These problems require learners to navigate complex linguistic constructions, identify relevant quantities, infer relationships, and ultimately convert textual descriptions into formal mathematical expressions. NLP techniques such as text classification, named entity recognition (NER), dependency parsing, and semantic role labeling serve as foundational components in this transformation process. One exemplary tool that operationalizes these capabilities is Photomath (Figure 4), which integrates Optical Character Recognition (OCR) with NLP to read both handwritten and printed math problems. Upon recognition, the tool dissects the linguistic structure of a given problem, maps it to the corresponding mathematical model, and then renders a step-by-step solution with explanatory annotations. Photomath exemplifies the practical synergy between language technologies and mathematical reasoning by showcasing how intelligent parsing systems can mediate the cognitive gap between how students encounter a problem and how it should be mathematically framed.

The pedagogical implications of NLP in mathematics education are profound. At its core, NLP supports the critical process of problem translation that enables learners to interpret mathematically rich texts with greater accuracy and confidence. Word problems, which often act as cognitive bottlenecks due to their dual linguistic and numerical demands, are rendered more accessible when NLP-driven systems scaffold the decoding process. Research by Winkler and Herman (2023) emphasizes that NLP tools can help learners develop representational fluency by modeling how to extract quantitative structures from natural language inputs. Additionally, Liu et al. (2022) found that NLP interventions were particularly beneficial for multilingual learners, enhancing comprehension by demystifying syntax and semantic ambiguities. These tools support metacognitive development by offering detailed solution paths, error identification, and opportunities for reflection. Students are not merely given answers but are prompted to follow the logic underlying each computational step. This interaction fosters strategic competence and encourages iterative problem-solving. Furthermore, NLP applications align well with the goals of universal design for learning (UDL), enabling diverse learners (including those with reading difficulties) to access mathematical content through multimodal feedback. As mathematics instruction continues to emphasize real-world problem contexts and open-ended tasks, NLP stands as a key enabler of equitable engagement, offering dynamic, language-sensitive scaffolds that adapt to the diverse cognitive and linguistic profiles of today's learners.

Concept Mastery and Skill Building

Conceptual mastery and procedural fluency remain enduring challenges in mathematics education (Lenz et al., 2024), particularly in conventional settings where instructional pacing often fails to accommodate the heterogeneous needs of learners. Mathematics is inherently hierarchical where gaps in early skills compound over time, which makes recovery difficult without individualized support. AI offers a transformative pathway to address these challenges by tailoring instruction to each learner's developmental stage (Zreik, 2024). One prominent example is DreamBox Learning (Figure 5), an adaptive learning platform that leverages AI-driven algorithms to diagnose skill levels, adjust content in real time, and guide students through conceptually coherent progressions. By continuously analyzingperformance patterns (e.g., response time, error types, and problem-solving strategies), the tool constructs personalized learning pathways that reinforce foundational skills while gradually introducing more complex tasks. Its pedagogical design emphasizes both conceptual depth and automaticity, which offer learners repeated exposure, varied representations, and real-time correction mechanisms. Within this type of pedagogical tool, AI becomes an active facilitator of mastery-oriented learning that supports differentiated instruction at scale. This degree of adaptivity also enables the early detection of learning plateaus, allowing timely pedagogical interventions before misconceptions solidify. Moreover, it deliberately shifts the instructional paradigm from reactive remediation to proactive advancement aligned with each learner's cognitive readiness.

Modeling learner cognition in real time and adapting instructional sequences accordingly support the development of relational understanding. Mustafa (2024) confirmed that adaptive learning environments improve learning outcomes and cognitive engagement by aligning tasks with the learner's zone of proximal development. In parallel, Arif and Nurhayati (2022) demonstrated that real-time personalization not only enhances efficacy in mastering core skills but also strengthens learners' self-efficacy, persistence, and confidence in navigating mathematical complexity. Importantly, the value of AI-based learning extends beyond procedural fluency. The deployment of AI chatbots as surrogate tutors has been shown to refine analytical reasoning and mathematical discourse (Busuttil & Calleja, 2025; Lee & Yeo, 2022; Pepin et al., 2025). Active learning that emphasizes experiential transfer over abstract repetition is exemplified in the work of Trindade et al. (2025), who explored simulation-based environments that integrate AI and spreadsheet automation to support workplace-relevant mathematical modeling. Similarly, Vintere et al. (2024) underscored the role of dynamic visual platforms in promoting self-directed exploration, particularly in understanding functions, graphs, and transformations. These teaching and learning environments foster conceptual visualization and allow learners to iteratively build and test hypotheses. Collectively, these developments suggest that AI is actively reconfiguring the pedagogical architecture of mathematics education. Rather than simply accelerating task completion, AI-based environments are increasingly designed to cultivate conceptual depth, procedural fluency, and strategic thinking.

Interactive Visualization and Simulation

AI-enhanced visualization and simulation tools are redefining how students engage with abstract mathematical ideas by transforming static representations into dynamic, interactive experiences. These tools respond to one of the core challenges in mathematics education by making highly symbolic and often decontextualized content intelligible to learners who struggle with purely formal reasoning (Yeo et al., 2024). More importantly, enabling students to manipulate parameters, construct geometric objects, and observe real-time changes in algebraic and graphical relationships supports conceptual learning through experimentation and visual reasoning. Among the most prominent tools in this space are GeoGebra and Desmos (Pope, 2022). GeoGebra (Figure 6), long recognized for its utility across geometry, algebra, and calculus, now includes AI-driven features that offer intelligent hints, automated feedback, and exploratory guidance (Hamady et al., 2024; Owusu et al., 2023). These affordances enable formative assessment and foster inquiry-based learning. Desmos, on the other hand, integrates graphing and simulation tools with an AI engine capable of interpreting student intent, providing real-time scaffolding, and adapting prompts based on learner input. Together, these digital learning platforms advance a constructivist model of mathematics instruction, where learners construct meaning through interaction, exploration, and visual experimentation.

In practice, AI-enhanced visualization and simulation systems act as cognitive amplifiers that enable learners to develop mathematical intuition through interactive feedback. Rather than memorizing isolated rules, students engage with representations that reveal the behavior of functions, transformations, and statistical relationships (Almusaed et al., 2023). These environments promote conceptual discovery and allow learners to test conjectures, refine misconceptions, and iteratively build meaning through active exploration and immediate response to input (Walter, 2024). A growing body of research affirms the efficacy of dynamic visualizations in deepening mathematical understanding. Abdulrahaman et al. (2020) emphasized that bridging symbolic and graphical representations enhances representational fluency, particularly for learners who struggle with abstract reasoning. When integrated with AI, these systems further adapt to learners' needs by modulating difficulty levels, identifying conceptual errors, and differentiating instruction accordingly. They also support inclusive and equitable access to mathematics by maintaining usability across diverse educational settings, including remote and resource-constrained environments (Ningsih et al., 2024). Moreover, these systems align with UDL principles by offering multiple modalities (visual, symbolic, and textual) for engaging with mathematical content (Saborío-Taylor & Rojas-Ramírez, 2024). In blended and flipped classrooms, they function as autonomous learning companions, enabling learners to revisit concepts at their own pace while receiving guided support through AI-driven feedback. The cumulative result is a pedagogical environment where both students and educators are empowered to engage with mathematics in more meaningful and intellectually rigorous ways.

Collaborative and Social Learning

Collaborative and social learning approaches in mathematics education position dialogue, peer interaction, and co-construction of knowledge as central to conceptual development. Yet orchestrating productive mathematical discourse poses significant pedagogical challenges, especially in digital or large-scale settings. AI has introduced a new layer of support by facilitating structured collaboration, guiding peer-to-peer engagement, and generating formative feedback responsive to both individual and group dynamics (Kovari, 2025). AI systems now assist in organizing shared problem-solving sessions, analyzing student contributions, and prompting metacognitive reflection, thereby expanding opportunities for collective mathematical reasoning. These affordances are exemplified in platforms such as Mathshare (Figure 7), which integrates step-by-step problem breakdown with shared digital workspaces and AI-mediated guidance. By enabling learners to articulate reasoning, critique peer solutions, and engage in joint exploration, Mathshare embodies the social constructivist principle that mathematical understanding is constructed through interaction, negotiation, and shared meaning-making.

The pedagogical utility of AI in collaborative learning environments lies in its capacity to scaffold both the cognitive and social dimensions of problem-solving. By requiring students to justify each procedural step and then share their solution process with peers, AI-supported platforms promote elaborative talk and self-explanation (Muhamad Fadzil & Osman, 2025). In this context, AI does not merely evaluate correctness but identifies logical inconsistencies, highlights incomplete reasoning, and offers real-time prompts that encourage revision and peer discussion. This iterative, socially mediated feedback loop transforms collaboration into a high-impact learning experience, where students refine their ideas through collective inquiry rather than solitary effort. Moreover, AI-mediated collaboration supports equity and inclusion by ensuring that all learners (regardless of prior confidence or performance) can meaningfully contribute to group tasks. These environments are especially powerful in distance and hybrid learning settings, where the sense of academic community is often fragile. Mohamed et al. (2024) emphasized that fostering connection and interdependence through digital platforms is crucial for sustaining learner motivation and persistence. In such contexts, AI-driven analytics can monitor participation, flag imbalances in group interaction, and suggest instructional interventions, ensuring that collaborative learning remains both productive and inclusive. As mathematics education increasingly embraces 21st-century competencies, AI-supported platforms offer a pedagogical infrastructure for cultivating not only mathematical reasoning but also the social-emotional and collaborative skills foundational to lifelong learning.

Gamified Learning Experiences

Gamification in mathematics education has gained traction as a powerful means of enhancing student engagement, particularly among learners who may otherwise experience disengagement or anxiety toward mathematical tasks (Alt, 2023; Elmawati et al., 2023; Espinosa-Pinos et al., 2024). At its core, gamification refers to the integration of game-like elements (e.g., points, levels, challenges, and rewards) into non-game contexts to increase motivation, persistence, and enjoyment (Mustafa et al., 2022; Valderama et al., 2022). These mechanics not only foster a sense of accomplishment but also encourage repeated engagement, which is crucial for mastering mathematical concepts. When combined with AI, gamified environments become not only entertaining but also adaptive in a way that it promotes real-time calibration of content difficulty, pacing, and feedback according to learner performance (Gómez Niño et al., 2024; Tan & Cheah, 2021). These AI-driven systems harness gameplay data to monitor progress, detect patterns in learning behavior, and personalize the trajectory of mathematical tasks. Prodigy Math (Figure 8), for example, exemplifies this approach by embedding AI algorithms that analyze player interactions and dynamically adjust problem complexity, ensuring an optimal balance between challenge and skill. This fusion of cognitive adaptivity and motivational design represents a novel pedagogical architecture in which learning and play are deliberately intertwined.

The integration of AI into gamified learning environments has significant pedagogical implications, particularly in its capacity to foster sustained engagement and promote long-term retention of mathematical concepts. By situating mathematical practice within immersive and reward-based contexts, gamification supports intrinsic and extrinsic motivation (Alt, 2023). Research has shown that when game-based learning environments are thoughtfully designed, it activates learners' attentional and affective systems that enhances their cognitive receptivity and deep processing of content (Chen et al., 2023). Furthermore, AI-enabled gamification introduces a feedback-rich ecology in which learners receive immediate, context-sensitive responses that guide self-correction and reinforce mastery. These systems are uniquely positioned to support spaced repetition, incremental difficulty progression, and strategic retrieval. Importantly, gamified platforms also foster a growth mindset by allowing learners to fail safely, iterate strategies, and receive rewards not merely for accuracy but for effort and perseverance (Lee et al., 2023). As mathematics educators seek to cultivate both skill proficiency and learner resilience, AI-powered gamification emerges as a compelling approach that aligns cognitive challenge with motivational design, supporting deeper and more durable learning outcomes.

Digital Escape Rooms

Digital escape rooms represent an emerging innovation in mathematics education, blending game-based mechanics with inquiry-driven learning to create immersive, problem-centered instructional environments. These platforms challenge students to solve a series of interconnected mathematical tasks embedded within a narrative structure, requiring them to apply logical reasoning, pattern recognition, and quantitative analysis to unlock progression through the storyline (Sidekerskienė & Damaševičius, 2023; Stohlmann, 2023). As students encounter clues and puzzles, they engage in active learning processes that elevate the cognitive demands of problem-solving beyond isolated drill exercises. The integration of AI enhances these environments by enabling adaptive task sequencing, real-time hint generation, and dynamic difficulty adjustment tailored to individual performance (Pinto et al., 2024). One notable tool in this space is AI Escape (Figure 9), which provides a customizable platform for educators to embed curriculum-aligned mathematical content into interactive digital escape room scenarios. With built-in AI capabilities for performance tracking and collaborative engagement, it exemplifies the pedagogical possibilities of intelligent, gamified learning environments.

The pedagogical value of AI-powered digital escape rooms lies in their capacity to convert abstract mathematical principles into situated, collaborative problem-solving experiences. Rather than engaging with content passively, students must activate and apply their mathematical knowledge (e.g., solving equations, recognizing patterns, and testing strategies) to advance through contextually rich challenges. This process fosters deeper conceptual understanding, particularly when students are encouraged to revise their approaches within a low-stakes, exploratory environment (Damaševičius & Sidekerskienė, 2023). AI integration plays a crucial role in ensuring that tasks remain appropriately challenging by dynamically adjusting difficulty and offering real-time feedback tailored to individual and group performance. Moreover, the collaborative design of these environments supports the development of communication and teamwork skills, as students work together to interpret clues and resolve interdependent tasks—skills essential not only in mathematics but across academic and professional contexts (Luque-Sánchez & Montejo-Gámez, 2023). Continuously monitoring learner interaction allows AI systems prompt under-engaged students, scaffold struggling groups, and highlight emerging misconceptions. These strategies aim to sustain engagement and maximize learning gains. As Damaševičius and Sidekerskienė (2023) argue, this fusion of adaptive learning technologies with immersive narrative structures offers a compelling model for 21st-century mathematics instruction that is interactive, inclusive, and intellectually rigorous.

AI and the Future of Mathematics Education

Hyper-Personalized and Neuroadaptive Learning

The future of mathematics education will be increasingly defined by learning systems that respond not only to performance data but also to the learner's cognitive and emotional states (Schukajlow et al., 2023). Traditional adaptive technologies adjust content based on correct or incorrect answers, but the next generation of AI-driven systems will incorporate neuroadaptive interfaces and cognitive modeling that respond to learners' real-time mental load, emotional responses, and attention levels (Saranya & Sankaradass, 2023; Trigka et al., 2024). Through the integration of wearable neurotechnology and brain-computer interfaces (BCIs), these systems will be able to detect patterns of disengagement, frustration, or cognitive overload by analyzing neural signals, gaze behavior, and physiological responses (Armani et al., 2023). This level of granularity will enable platforms to dynamically adjust pacing, simplify representations, or offer timely scaffolds based on individual readiness. For instance, if a learner begins to show signs of strain, the system might slow down, switch strategies, or revert to prerequisite content to restore flow. Such neuroadaptive responsiveness holds promise for learners who experience mathematics anxiety or executive function challenges. Tailoring instruction to fit the learner's cognitive profile in real time will allow AI tools transform mathematics education into a more intuitive, supportive, and affect-sensitive experience (Armani et al., 2023; Richard et al., 2022).

Immersive and Embodied Mathematical Experiences

Advances in mixed reality and conversational AI are also poised to transform mathematics instruction into a multisensory, embodied experience (Na & Sung, 2025; Santos et al., 2025). AI-powered holographic tutors will act as interactive teaching agents capable of delivering real-time verbal explanations, adjusting to learners' affective cues, and guiding problem-solving processes with responsive dialogue (Huang et al., 2021). These tools hold promise for increasing engagement and accessibility in diverse classrooms, where students often require differentiated instruction and multimodal learning opportunities (Lai & Cheong, 2022). In particular, these agents can be deployed within mixed-reality environments where students can visualize mathematical concepts in three dimensions, manipulate virtual objects through gestures, and explore abstract structures through spatial interaction (Kulkarni et al., 2024). Rather than studying equations on a two-dimensional screen, students will interact with vector fields, integrals, and functions as dynamic, manipulable forms. This spatial engagement will improve conceptual understanding, particularly for visual and kinesthetic learners who benefit from experiential learning. The ability to step into a mathematical environment, observe transformations from multiple perspectives, and construct meaning through guided manipulation will redefine how abstract ideas are internalized and applied (Bulut et al., 2024).

Generative Problem Design and Contextual Relevance

Generative AI will enable the design of highly contextualized mathematical tasks that reflect students' interests, goals, and lived experiences. Unlike traditional platforms that rely on static banks of problems, AI-powered systems will dynamically generate problem sets using models trained on diverse pedagogical corpora (Christ et al., 2024). This approach can help educators differentiate instruction more effectively, which allows them to meet students at their individual levels while maintaining curricular relevance. These systems will incorporate information about a learner's domain preferences, prior progress, and even career aspirations to create tasks that are both challenging and personally meaningful. For example, a student exploring algebra might engage with data-driven models related to space exploration, while another studying statistics could analyze social media trends or climate projections. By grounding abstract concepts in real-world applications, these generative systems will foster engagement and support knowledge transfer across disciplines. This evolution will transform problem-solving from a procedural activity into a cognitively rich and contextually grounded experience.

Narrative-Driven and Game-Based Instruction

The fusion of AI with interactive storytelling engines will give rise to narrative-based mathematics instruction, where students engage with mathematical content through evolving plots and immersive simulations (Moore et al., 2024). These AI-driven platforms will construct dynamic stories that adapt in real time based on learner performance, decision-making, and emotional engagement. This approach holds pedagogical importance for both digital game-based and serious game applications in the mathematics classroom, where narrative contexts can drive sustained attention, critical thinking, and meaningful learning outcomes (Arif et al., 2025; Hui & Mahmud, 2023; Hussein et al., 2022). Each mathematical challenge will become part of a broader narrative arc, prompting learners to apply concepts to navigate a fictional scenario, solve a mystery, or complete a mission. This instructional shift toward narrative structure will increase motivation and deepen conceptual understanding by embedding mathematics in emotionally resonant and problem-rich environments. Rather than viewing mathematics as a series of isolated exercises, students will experience it as a tool for exploration and resolution within a cohesive story. The ability of AI to modify the storyline based on learner behavior ensures that every experience remains personalized, immersive, and pedagogically aligned.

Creativity Augmentation and Mathematical Inquiry

Future AI systems will extend their role from scaffolding known concepts to co-participating in the creative processes of mathematics. As one of the first major applications of AI in creativity, these tools will support learners in exploring open-ended questions and engaging in mathematical discovery. These systems will be capable of engaging in conjecture generation, symbolic reasoning, and proof exploration in collaboration with learners (Berson et al., 2023). In the classroom, this methodology can facilitate inquiry-based learning environments where students work alongside AI to explore mathematical ideas. Using models trained on formal logic and advanced symbolic manipulation, AI will help students formulate original problems, visualize abstract relationships, and examine multiple pathways to a solution. This approach will empower students to participate in mathematical creativity, moving beyond consumption toward the generation of new ideas. Instead of merely solving teacher-assigned problems, learners will propose their own conjectures, iterate on hypotheses, and explore the underlying structure of mathematical principles. AI will act not as a replacement for human creativity but as a catalyst that extends the learner's capacity for invention and abstraction (Garcia, 2024).

AI-Driven Policy, Curriculum, and Teacher Development

AI will also play a transformative role in shaping mathematics education at the systemic level. National education systems and school networks will increasingly integrate AI-powered learning analytics into their curriculum development and assessment frameworks (Mishra et al., 2024; Okewu et al., 2021). These systems will provide continuous, data-informed insights into learner progress, concept mastery, and engagement patterns, allowing for agile adjustments to curriculum content and instructional priorities. Simultaneously, teacher development will become more personalized through AI-driven feedback systems that analyze classroom discourse, track instructional efficacy, and recommend targeted interventions. Teachers will receive actionable guidance on student misconceptions, effective pedagogical strategies, and differentiated support pathways. By embedding AI into teacher training and curriculum evaluation (Sperling et al., 2024; Tan et al., 2025), educational institutions will be better positioned to align instruction with the evolving demands of STEM-oriented economies and the needs of diverse learners.

A Redefined Landscape for Mathematics Education

The cumulative impact of these developments suggests a fundamental redefinition of mathematics education in the age of AI (Richard et al., 2022; Zreik, 2024). Instruction will no longer proceed through static, one-size-fits-all pathways but will instead unfold within responsive ecosystems that adapt to each learner's cognitive, emotional, and contextual profile. AI will serve as both a mentor and a collaborator that shapes instruction, augments insight, and supports the co-construction of knowledge. This reimagined paradigm will position mathematics as a dialogic, exploratory, and socially relevant discipline. The integration of neuroadaptive interfaces, immersive media, generative problem design, intelligent feedback, and AI-driven policies will produce learning environments that are rigorous yet humane, scalable yet personalized. As these technologies mature, they will offer educators and learners tools not just for mastering mathematics but for experiencing it as a deeply meaningful and creative pursuit. Regardless of background, ability, or learning style, this transformation promises greater access, deeper engagement, and a renewed sense of agency in the mathematical journey for all.

Conclusion

As AI continues to transform the educational landscape, its strategic integration into mathematics instruction offers profound opportunities to reimagine how students engage with one of the most foundational disciplines in the curriculum. As this chapter has demonstrated, AI possesses the capacity to rehumanize mathematics instruction by adapting to individual cognitive needs, personalizing learning trajectories, and transforming abstract content into deeply interactive experiences. These developments signal a pedagogical recalibration where mathematical thinking is no longer confined to symbolic procedures but cultivated through adaptive dialogue, multimodal representation, and creative inquiry. The implications of these innovations are both immediate and long-term. First, students must develop the metacognitive skills needed to engage critically with feedback, question algorithmic authority, and build resilience in navigating complex, adaptive learning environments. For mathematics educators, it is essential to develop critical competencies in AI literacy to understand how these systems shape feedback, structure learning experiences, and influence students' mathematical identities. Researchers must also interrogate the assumptions embedded within AI systems and examine their implications for equity, access, and epistemological diversity in mathematics education. Finally, policymakers must craft frameworks that support responsible innovation while safeguarding the disciplinary integrity and humanistic aims of mathematics instruction. If guided by pedagogical intentionality and ethical foresight, the integration of AI can fundamentally redefine what it means to learn mathematics in a connected and human-centered world.

Key Terms and Definitions

Adaptive Learning: Refers to the use of AI systems that dynamically adjust instructional content and pacing based on student performance and behavior.

Artificial Intelligence in Education (AIEd): Encompasses the application of AI technologies to enhance teaching, learning, and assessment across educational contexts.

Automated Assessment: Involves AI-powered tools that evaluate student work, provide real-time feedback, and support scalable grading in mathematics education.

Digital Escape Rooms: Interactive learning environments where students solve AI-supported mathematical challenges to progress through narrative-based scenarios.

Gamification: Refers to the integration of game elements into learning experiences, often supported by AI to personalize challenges and sustain student engagement.

Mathematics Education: Focuses on the discipline-specific study of teaching and learning mathematical concepts with emphasis on cognitive, technological, and pedagogical strategies.

Personalized Learning: Involves tailoring instructional experiences to individual learner profiles using AI to adapt content, feedback, and learning trajectories.