1. Introduction
We live in a pluralistic society that resists the standardization of a heterogeneous population. Every human being is distinct not only in race, ethnicity, first language, religion, age, sex, gender, ability, and home culture, but also in countless other ways. Rooted in the belief in the inherent worth of each individual, Grant [1] emphasizes education that respects and affirms diversity and individual differences, thereby fostering self-actualization. Because students come to schools from a wide range of backgrounds, many education scholars have problematized instructional practices that impose uniform goals, pedagogies, and content irrespective of students’ differences. Such practices, they argue, confine students to a “cookie-cutter” mold, failing to respond to diversity and instead promoting assimilation [2,3].
Educators have increasingly implemented differentiated and individualized instructional practices to respond to student diversity, applying these approaches with varying degrees of flexibility. Moreover, the advancement of artificial intelligence (AI) has made the practical implementation of such practices more feasible [4]. These approaches have become standard in both K–12 and higher education [5] and are particularly effective when combined with collaborative learning [6].
However, as indicated by the U.S. Department of Education [7], the terms differentiation, individualization, and personalization are often used inconsistently, and their meanings and applications vary across contexts. The phrases “individualized learning,” “differentiated learning,” and “personalized learning” are frequently treated as synonymous alternatives to the “one-size-fits-all” model (e.g., [8]). Furthermore, while some studies have sought to distinguish among these three practices [9,10], their definitions remain inconsistent. Such non-standardized usage makes it difficult to establish a single coherent theoretical foundation or developmental trajectory.
In this context, rather than adopting an essentialist stance toward these concepts, this paper follows the U.S. Department of Education’s framework to clarify their conceptual distinctions. Using this framework as a provisional guide, I elaborate on Bloom’s mastery learning in the following section, identifying it as a theoretical basis for individualized and differentiated learning. Given the multiple and sometimes conflicting theoretical roots of personalized learning, this positioning may be regarded as contentious. Finally, in light of the growing focus on technology and its potential to support personalized learning in medical education, I introduce current debates concerning personalized learning within the broader context of educational technology (EdTech).
2. Individualization, differentiation, and personalization as buzzwords in education
Since 1996, the U.S. Department of Education has published five editions of the National Education Technology Plan, offering educators guidance on integrating technology into their instructional practices to enhance learning outcomes in the United States. These plans also provide insight into the direction of American educational policy on technology in education. While the most recent plan, published in 2024, focuses on digital inequalities in education that became increasingly apparent during the COVID-19 (coronavirus disease 2019) pandemic [11], this section draws primarily on the 2010 and 2017 plans, which problematize the unreflective use of “individualization,” “differentiation,” and “personalization” as educational buzzwords.
The U.S. Department of Education [7,12] clearly distinguishes among individualization, differentiation, and personalization, acknowledging that educators often use these terms inconsistently. Each concept emphasizes a different aspect of learning—pace, method, or goal. Individualized learning emphasizes variations in students’ learning pace, allocating differing amounts of time to particular content according to each learner’s needs. Differentiation, by contrast, focuses on students’ diverse learning preferences and involves designing a variety of activities and instructional approaches to accommodate these differences.
Although both approaches share the assumption that all students aim toward the same learning goal—albeit at different speeds and through different methods—personalization does not adhere to a uniform goal. What differentiates personalized learning from the other two is how learning goals relate to students’ individual differences. In personalization, learning goals emerge from students’ unique characteristics, whereas in individualization and differentiation, those differences are considered only after goals are established. Personalization thus entails flexibility in adjusting learning goals, content, methods, and pace in response to each learner. It also promotes self-initiated learning experiences that reflect students’ interests and motivations. In this sense, personalized learning encompasses the elements of both differentiation and individualization: in a fully personalized environment, learning objectives, content, methods, and pace may all vary among students.
Building on the U.S. Department of Education’s framework [7], Bray and McClaskey [9] further clarify these distinctions by examining who—teacher or learner—primarily guides each practice. They argue that personalization centers on the learner, who directs learning according to personal interests, passions, and aspirations, while individualization and differentiation are teacher-directed. In the latter two, teachers select appropriate technologies and resources, provide scaffolding, and monitor student progress through assessment. In personalization, however, assessment itself becomes an integral part of the learning process rather than serving merely for or of learning. Ultimately, personalization aims to cultivate self-directed learners who monitor their own progress and reflect on their learning through mastery of both content and skills.
In practice, however, educational standards continue to define what students are expected to know and be able to do, and teachers remain accountable for ensuring those standards are met. The U.S. Department of Education [7] does not present standards and personalization as mutually exclusive. Rather, standards are treated as minimum expectations that remain open to adaptation. Instead of adopting a binary stance, personalization encourages teachers to design multiple, flexible learning pathways that respond to students’ needs.
In summary, individualization and differentiation both aim to adapt instruction based on student differences to achieve predetermined learning goals. These teacher-guided practices assume that all students can succeed when appropriate conditions are provided. Personalized learning, by contrast, values emergent learning goals that arise from individual differences, allowing variation in learning goals, content, methods, and pace. In the following section, I will discuss Bloom’s mastery learning as a theoretical foundation for individualization and differentiation, particularly concerning their focus on responding to students’ diverse learning characteristics.
3. Mastery learning as a foundation for individualized and differentiated learning
What any person in the world can learn, almost all persons can learn if provided with appropriate prior and current conditions of learning [13, p.132]
Risking a sweeping generalization, Bloom [13] concluded that approximately 95% of people without physical or emotional difficulties can learn anything, provided that past and present learning conditions are adequately maintained. He supports this theory through three key constructs: learning ability, learning pace, and learning conditions. Bloom challenges the first two constructs, which categorize students as “good” or “poor” learners and as “fast” or “slow” learners, respectively, as if these were fixed traits. Such assumptions implicitly ascribe immutable learning capacities and paces to individual students.
In contrast, the third construct posits that learning ability and pace are malleable and depend on the learning conditions provided. Bloom argues that these characteristics are not innate but constructed. For instance, “most students become very similar with regard to learning ability, rate of learning, and motivation for further learning when provided with favorable learning conditions” [13, p.135], in stark contrast to the widening disparities observed under unfavorable conditions. Although initial differences may exist among learners, these distinctions can become negligible when students receive appropriate support, such as constructive feedback and individualized corrective assistance.
Based on the belief that each learner can achieve specific objectives under favorable conditions, Bloom [13,14] proposed mastery learning, which emphasizes the adequate acquisition of prior knowledge as a prerequisite for subsequent learning. Prior experiences and outcomes serve as conditions for further learning, which teachers are responsible for maintaining in a favorable state. As an adaptive instructional approach, mastery learning encompasses multiple methods rather than a single strategy. Moreover, Bloom [13, p.127] cautions against romanticizing adaptation, as excessive adaptation may foster dependency, making students reliant on continual scaffolding. Thus, individualization and differentiation must be balanced with the goal of nurturing learners “with abilities and affective dispositions which will permit them to adapt to a wide variety of situations.”
Bloom’s mastery learning model centers on externally established goals and measurable achievement. For effective instruction, he stresses the importance of explicitly communicating learning goals so that students understand the expected outcomes. During instruction, errors and misconceptions must be promptly addressed, and individualized or differentiated activities should be offered to prevent the accumulation of misunderstandings. This approach highlights the value of formative evaluation, which provides diagnostic information to teachers and justifies adjustments in instructional strategies, thereby supporting individualized and differentiated learning [13,15]. Furthermore, students can employ formative assessments to gauge their progress, identify misconceptions, and improve subsequent learning outcomes.
Importantly, Bloom [13] does not view direct group instruction and individualization/differentiation as oppositional. Instead, he considers corrective feedback and individualized assistance—both responsive to students’ diverse learning processes and outcomes—as complementary to group instruction and integral to mastery learning strategies.
As shown above, unlike personalization, individualization and differentiation serve as instructional methodologies to help learners achieve externally defined goals within the mastery learning model. For Bloom, teachers are responsible for ensuring that students meet standards established externally. Adaptation, therefore, applies to instructional methods rather than learning objectives, reflecting his conviction that most students can learn almost anything under favorable learning conditions. In this model, assessments function instrumentally—to identify and address prior errors—rather than constituting learning in themselves. In the next section, I will introduce the concept of an adaptive curriculum as a theoretical basis for personalized learning, emphasizing the learner’s central role in determining learning goals.
Nonetheless, attributing the theoretical basis of individualization and differentiation exclusively to Bloom’s mastery learning remains provisional, especially when considering personalization. Many scholars still invoke mastery learning in discussions of personalized education despite its limited accommodation of student-driven goal setting. For instance, Davis et al. [16] situate mastery learning within the student-centered movement in health professions education, recognizing learners’ autonomy in defining their objectives and experiences beyond simply controlling learning pace and process—what I identify as personalization in this paper.
Bernacki et al. [5] also highlight the lack of theoretical consensus surrounding personalized learning, noting that only 6.5% of the studies they reviewed explicitly defined the concept. While scholars across disciplines emphasize individual learners and their needs, they observe a disciplinary divide: education researchers focus on learners’ interests and goals, whereas engineering and computer science researchers emphasize system adaptivity.
Furthermore, Plass and Pawar [10] conceptualizes personalization as an overarching framework encompassing both individualization and differentiation. He defines personalization as the design of adaptive systems that incorporate “differentiation to accommodate students’ interests, prior knowledge, and preferences, as well as individualization to accommodate students’ degree of special need or specific level of skill or ability by altering a learning progression” [5, p.1684]. In this view, personalization systems are informed by cognitive, motivational, affective, and sociocultural variables but differ from adaptable environments, which grant learners agency in goal-setting. Consequently, the understanding of personalization by Plass and Pawar [10] is narrower than the definition adopted in this paper, which extends to include learner adaptability in his sense.
Despite the varied theoretical positions, personalized learning has attracted increasing interest in health sciences higher education for its potential to enhance student engagement and learning outcomes by addressing diverse learner needs [17]. In response to persistent challenges in adaptive teaching—such as assessing individual learning needs, staff and resource shortages, and the need for faculty development among medical educators [18]—technological advancements, including AI, learning management systems, adaptive learning platforms, and learning analytics, may reduce barriers to personalization [17,19]. Yet in medical education, technology has often neglected individual learner differences, prompting calls for more adaptive content aligned with students’ needs [20]. Considering the growing role of EdTech in implementing personalized learning, the following section examines current debates surrounding personalization in relation to technological innovation.
4. Personalized learning meets technology
Technology increasingly is being used to personalize learning and give students more choice over what and how they learn and at what pace, preparing them to organize and direct their own learning for the rest of their lives [11, p.7].
Although personalized learning is not inherently dependent on EdTech, the two have become increasingly intertwined in contemporary education [21]. The rapid advancement of EdTech exemplifies the potential of personalized learning within the broader framework of technological determinism, which emphasizes the strong interconnection between technological innovation and social transformation [22]. EdTech companies have redefined traditional learning environments by creating digital tools, platforms, and resources that are believed to support personalization in learning [23]. In particular, AI technologies—such as machine learning, natural language processing, and data mining—are being leveraged to deliver “tailored learning experiences based on individual learner characteristics, including learning styles, prior knowledge, and preferences” [23, p.398]. Additionally, other emerging technologies, including gamification, virtual reality, and augmented reality, have been integrated with AI to further promote personalized learning. Supporting this optimistic perspective, Hong and Kim [24] conducted a systematic literature review demonstrating the effectiveness of EdTech in medical education, particularly the ways in which AI enables personalized learning through adaptive assessment and feedback mechanisms.
Nevertheless, the effectiveness of integrating EdTech into personalized learning remains contested. On the one hand, the U.S. Department of Education [12] underscores the potential of technology-enabled personalized learning to transcend the physical limitations of schools, enabling students to learn anywhere with internet access. This spatial flexibility may help alleviate geographic and socioeconomic barriers by offering affordable access to high-quality online courses and mentoring opportunities. Moreover, students can collaborate with peers globally and share their work with wider audiences, thereby extending both learning opportunities and recognition beyond local contexts. In line with this perspective, a systematic review of technology-enhanced adaptive and personalized learning reported predominantly positive outcomes across key dimensions—affective (95.5%), cognitive (89.6%), skill-based (66.7%), and behavioral (100%) [25]. However, Bernacki et al. [5] cautioned that such findings may not fully account for the inherent nonstandardizability of personalized learning, since learning goals vary across individuals and are implemented through diverse, context-dependent activities in this model.
Conversely, education scholars have raised critical concerns about the unreflective adoption of technology in personalized learning [19,20]. Ali et al. [17], for instance, highlight potential challenges related to inaccuracy, misinformation, privacy, data security, and the digital divide, particularly in the integration of AI into medical education. Similarly, Herold [26] critiques the enthusiasm for technology-driven personalization, pointing to the lack of a clear, unified definition and the limited empirical evidence substantiating its effectiveness. He further warns that such initiatives risk reintroducing behavioristic instructional models by channeling students into decontextualized, isolated learning pathways and by increasing vulnerability to data privacy breaches in the name of data-driven personalization. Echoing these concerns, Brass and Lynch [27] problematize the convergence of personalized learning and EdTech within the broader context of marketization and commodification, arguing that the development and implementation of personalized learning have often been driven by networks of education, business, government, and philanthropy. Both for-profit and non-profit providers outside the public education system play a dominant role in shaping the discourse and practice of personalized learning, frequently prioritizing market imperatives over pedagogical integrity.
In summary, the integration of EdTech and personalized learning represents a double-edged phenomenon. On the one hand, EdTech has the potential to advance personalized learning by transforming educational environments, overcoming physical and economic limitations, and enhancing students’ cognitive and affective outcomes. On the other hand, this optimistic vision is tempered by critiques of decontextualized, behavioristic practices and concerns about equity, data ethics, and commercialization. As scholars have critically noted, EdTech-driven personalized learning may function as “an instrument and effect of a radical shift from social democratic to neoliberal governance” [19, p.17].
5. Conclusion
This paper has offered a provisional distinction among individualization, differentiation, and personalization in education, recognizing that these terms are frequently used interchangeably in practice. While the first two approaches—individualization and differentiation—originate from teacher-led instruction and assume that students reach predetermined goals through differentiated activities, personalization emphasizes that learning goals, activities, and content are shaped primarily in response to students’ unique needs and interests. Unlike individualization and differentiation, personalization does not presume uniform goals across learners but instead foregrounds diversity in aims, processes, and outcomes, as illustrated in contrast with mastery learning. As Bernacki et al. [5] observe, personalized learning operates in a “many-to-many-to-many fashion” (p.1677), underscoring variations among students, activities, outcomes, and goals. Given this inherent multiplicity, the nonstandardizability of personalized learning is inevitable, as it accommodates a wide range of distinct activities and learning pathways within the same overarching framework.
In this sense, this essay is limited in addressing ongoing debates regarding the integration of technology into personalized learning, since key terms may be conceptualized differently across studies. Although this essay provisionally adopts the U.S. Department of Education’s definitions to differentiate among individualization, differentiation, and personalization, much of the existing research employs these terms inconsistently or classifies them in divergent ways. Nevertheless, given the growing emphasis on technology’s potential to enhance student engagement and improve learning outcomes by addressing diverse learner needs in medical education [17], it remains critical to engage with current debates on personalization—even when, within this paper’s framework, such discussions might more appropriately align with individualization or differentiation.
Importantly, the double-edged nature of technology-enhanced personalized learning suggests that it cannot be uncritically celebrated or outrightly dismissed. Because of its “many-to-many-to-many” character, contradictory practices may coexist under the same label of personalized learning. Moreover, ethical challenges—such as inaccuracy, misinformation, privacy and security risks, and the digital divide—emerging from the integration of technology into personalization in medical education underscore the need for an ethical framework to guide its responsible implementation [17,28]. Addressing these issues requires collaboration between educators and researchers to develop ethical and inclusive strategies that encourage continuous reflection on pedagogical practices. Such reflection should prompt essential questions: What assumptions underlie this practice? What costs accompany it? What expected or unintended outcomes might it produce? Without deliberate reflection of this kind, educators and researchers risk inadvertently causing harm to learners, despite their well-intentioned efforts to address individual differences.
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