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. 2025 Jul 1;25:969. doi: 10.1186/s12909-025-07565-1

Personalised learning in higher education for health sciences: a scoping review

Majid Ali 1,2,✉, Izyan A Wahab 2,✉, Hasniza Zaman Huri 2, Muhamad Saiful Yusoff 3
PMCID: PMC12217284  PMID: 40598075

Abstract

Background

Personalised learning approaches have gained increasing attention in higher education, particularly in health sciences, due to their potential to enhance student engagement and learning outcomes. However, the implementation and effectiveness of personalised learning strategies in health professions higher education remain unclear. This scoping review aims to map the existing literature on personalised learning in health sciences higher education, identifying key concepts, gaps in knowledge, and areas for future research.

Methods

We conducted a comprehensive search of five electronic databases: PubMed, Scopus, Google Scholar, Educational Research Complete, and JSTOR. The search strategy employed various combinations of keywords related to personalised learning and health sciences higher education. Studies published in English between 2000 and 2024 were included. Two independent reviewers screened titles, abstracts, and full texts. Data were extracted using a data extraction form. The review followed the methodological framework outlined by Arksey and O’Malley and adhered to the PRISMA-ScR guidelines.

Results

The initial search yielded 1,247 records, with another 15 identified through other sources. After removing duplicates, 583 records were screened, resulting in 42 full-text articles assessed for eligibility. Ultimately, 11 studies met the inclusion criteria and were included in the qualitative synthesis. The review identified various personalised learning approaches implemented across different health science disciplines, including medicine, nursing, pharmacy, and dentistry. Key themes emerged around adaptive learning technologies, individualised feedback mechanisms, and student-centred curriculum design. Challenges in the implementation and assessment of personalised learning strategies were also highlighted.

Conclusions

This scoping review provides a comprehensive overview of personalised learning approaches in health sciences higher education. While the findings suggest potential benefits of personalised learning, they also reveal a need for more rigorous research to evaluate its effectiveness and long-term impact on student outcomes. Future studies should focus on standardising assessment methods, exploring the role of technology in facilitating personalised learning, and investigating the scalability of these approaches across different health science disciplines.

Protocol registration link

https://osf.io/nu4yj/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12909-025-07565-1.

Keywords: Personalised learning, Individualised learning, Adaptive learning, Health sciences education, Scoping review

Background

Personalised learning, which customises educational experiences to cater to individual students’ needs, preferences, and learning styles has emerged as a vital approach in modern education. Grounded in constructivist theories, personalised learning promotes active, self-directed learning and the creation of individualised learning pathways [1]. The adoption of personalised learning in higher education has been accelerating, driven by its potential to significantly enhance learning outcomes by addressing the diverse needs of students. Traditional teaching methods in higher education, often characterised by a one-size-fits-all approach, frequently fall short of accommodating students’ varied learning styles, abilities, and interests. This is particularly evident in health sciences education, where the complexity and breadth of knowledge necessitate tailored instructional strategies to help students reach their full potential [2]. Technological advancements, such as learning management systems, adaptive learning platforms, and learning analytics, have further enabled the implementation of personalised learning in various educational contexts.

The origins of personalised learning date back to early 20th-century educational theorists like John Dewey, who championed individualised instruction [3]. Over the years, various definitions and models of personalised learning have evolved, reflecting a range of perspectives and approaches to implementing this concept in educational settings [4]. At its core, personalised learning involves tailoring learning activities, materials, and assessments to meet each student’s unique needs and preferences.

Research has highlighted several benefits of personalised learning in higher education. This approach has been linked to enhanced student engagement, satisfaction, and retention, as well as improved academic performance and the development of critical thinking and problem-solving skills [5]. Furthermore, personalised learning can effectively address the needs of diverse student populations, including those with learning disabilities, English language learners, and non-traditional students [6].

In the realm of health sciences education (medicine, dentistry, nursing, pharmacy), personalised learning holds promise for boosting student engagement and academic achievement by offering tailored instructional strategies that accommodate the demanding and varied nature of health sciences curricula [7]. This approach can also help mitigate student attrition by addressing individual learning preferences and needs. Despite these advantages, the literature on personalised learning in health sciences higher education is fragmented and varied. Different definitions, models, and methods have been proposed and implemented across various fields and disciplines, leading to a lack of cohesive understanding in this area [8]. A comprehensive and structured synthesis of the available evidence is necessary to provide a clear overview of the current state of research and to identify future research directions.

Scoping reviews are an appropriate methodology for achieving this objective, as they aim to map key concepts, theories, and sources of evidence in a given research area, providing a broad overview of the literature and identifying research gaps [9, 10]. This scoping review aims to map the current literature on personalised learning in health sciences higher education, including its definitions, implementation strategies, benefits, and limitations.

Objectives

The primary objectives of this scoping review are to:

  • Identify definitions of personalised learning in the context of health sciences at higher education.

  • Examine the strategies for implementing personalised learning and their evaluations in health sciences at higher education.

  • Outline the benefits and limitations of personalised learning in health sciences at higher education.

  • Discuss the implications of personalised learning in the context of health sciences at higher education.

Methods

The findings of this scoping review are to be reported in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines [11]. The protocol has been registered prospectively on the Open Science Framework (https://osf.io/nu4yj/) and published previously in BMC Systematic Reviews [12].

Information sources and search strategy

A comprehensive search of electronic databases was conducted to identify relevant literature. The following databases were searched: PubMed, Scopus, Google Scholar, Educational Research Complete (EBSCO), and Journal Storage (JSTOR). The search strategy was designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education.

The following search strategy was used to identify relevant studies: (“Personalised learning” OR “Individualised learning” OR “Customised learning” OR “Tailored learning” OR “Adaptive learning” OR “Individualised instructions” OR “Individualised guide” OR “Personalised instructions” OR “Personalised guide” OR “Learning preferences” OR “Student-centred learning” OR “Student-centred instructions” OR “Learner-centred learning” OR “Learner-centred instructions”) AND (“Health sciences” OR “Healthcare sciences” OR “Higher education” OR “College” OR “University” OR “Academia”). The search terms were adapted for each database based on their specific search functionalities. The database-specific search strings and the number of records retrieved are detailed in the supplementary file, and the PRISMA flow diagram (Fig. 1) outlines the process of study selection.All searches were limited to articles published in English between 2000 and 2024. The reference lists of included articles were also hand-searched to identify additional relevant studies. The search strategy was designed to focus specifically on personalized learning in health sciences higher education to ensure the relevance of the findings. While a broader search might capture additional perspectives, it could also dilute the specificity required for this review.

Fig. 1.

Fig. 1

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PRISMA flow diagram. (Adapted from Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71)

Eligibility criteria

Inclusion criteria:

  • Published in the English language.

  • All study types (research as well as relevant reviews).

  • From all geographical locations.

  • Focused on personalised learning in higher education health sciences (medicine, pharmacy, nursing, dentistry, physiotherapy, radiology).

  • Published between 2000 and 2024.

  • Both peer-reviewed and non-peer-reviewed.

Exclusion criteria:

  • Published in a language other than English.

  • Published before 2000.

Selection process

The screening and selection process involved several steps to ensure comprehensive and systematic identification of relevant literature. First, the search results from each database were imported into EndNote to remove duplicates. Two reviewers screened the titles and abstracts of the identified articles against the eligibility criteria independently. Full-text assessments were performed for potentially eligible articles, and any disagreements between the reviewers were resolved through discussion or consultation with a third reviewer. The reasons for excluding articles during the full-text screening were recorded, and a PRISMA flow diagram was used to illustrate the selection process (Fig. 1).

Data extraction

A data extraction form was developed based on JBI guidelines and pilot-tested on a subset of included articles [10]. The following data were extracted from each article: authors and year, country/geographical area, title, aim, healthcare field, study population/sample size, topic (learning and teaching, assessment, feedback), key findings, and research gap.

Data extraction was performed by the same two reviewers independently, with any discrepancies resolved through discussion or consultation with a third reviewer. The reviewers contacted the authors of selected papers for clarification when necessary.

Data synthesis

A thematic synthesis approach was used to present and analyze the extracted data, following the steps outlined by Thomas and Harden [13]. This involved the following stages: (1) familiarization with the data, (2) development of descriptive themes, (3) generation of analytical themes, and (4) refinement and synthesis of themes. Key themes and topics related to personalised learning in health sciences higher education, including definitions, models, and implementation strategies, were identified. Additionally, the benefits and limitations of personalised learning in health sciences higher education were summarised. The results reported were discussed and approved by all the reviewers.

Ethical considerations

As this scoping review included publicly available published material, ethical approval was not required.

Results

The process of search and article selection was performed based on the PRISMA 2020 Flow Diagram (Fig. 1). The search identified 1,262 records. After removing the duplicates, titles and abstracts of 583 records were screened, and 541 records were removed. Full-texts of 42 articles were assessed for eligibility, and 31 articles were further removed for the reasons mentioned in Fig. 1. Eventually, 11 articles were included in the scoping review. Data extraction from the included articles is presented in Table 1. Figure 2 illustrates the graphical presentation of the results.

Table 1.

Characteristics of included articles

Author(s), Year Country Healthcare Field Study Population Study Design Key Findings Research Gap
Wolf, 2010 [4] USA General Varied Review Definitions and models of personalised learning Lack of empirical studies specifically within health sciences
Dziuban et al., 2016 [6] USA General Varied Review Enhanced academic outcomes and skill development Need for more health-specific research on personalised learning
Alamri et al., 2021 [7] Saudi Arabia General University Students Mixed Methods Addressing diverse student needs effectively Implementation of personalised learning in health sciences within low-resource settings
Bernard et al., 2014 [8] Canada General University Students Meta-analysis Improved critical thinking and problem-solving skills Exploration of personalised learning’s impact on specific health sciences disciplines
Prinsloo & Slade, 2017 [14] South Africa General Varied Qualitative Supports inclusiveness for disabled students Addressing equity in technology access within health sciences
Siemens, 2013 [15] Canada General Varied Review Learning analytics can inform personalized instruction but raise privacy concerns Need for strategies addressing privacy and security concerns in health sciences
Gašević et al., 2015 [16] Various General Varied Quantitative Adaptive technologies enhance learning outcomes but require ethical considerations Ethical considerations in data use specific to health sciences
Fidalgo-Blanco et al., 2016 [17] Spain General University Students Mixed Methods Hybrid xMOOC/cMOOC approach enhances student collaboration Scalability of hybrid models within health sciences education
Khalil et al., 2023 [18] Various General Varied Systematic Review Learning analytics supports inclusiveness for disabled students Longitudinal studies on long-term effects of personalized learning in health sciences
Garrison & Vaughan, 2013 [19] Canada General University Students Case Study Blended learning models require institutional support and faculty engagement Need for faculty training and development specific to health sciences
Pane et al., 2015 [20] USA General Varied Quantitative Improved student engagement, satisfaction, and academic performance Challenges in technology implementation and scalability specific to health sciences
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Fig. 2.

Fig. 2

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Graphical presentation of the results

Definitions of personalised learning in health sciences higher education

The review identified several key studies that provided definitions and models of personalised learning in the context of health sciences higher education. These studies highlight the diverse interpretations and implementations of personalised learning across various disciplines and educational settings. Wolf (2010) provides an extensive review of the definitions and models of personalised learning [4]. Wolf emphasises the customization of learning activities, materials, and assessments to meet individual learner needs. The study highlights the evolution of personalised learning from early educational theories to modern implementations using digital technologies. It, however, identifies a lack of empirical studies specifically within the health sciences. Dziuban et al. (2016) discuss the impact of personalised learning on academic outcomes and skill development [6]. The study defines personalised learning as an educational approach that adapts to individual student needs through various technologies and instructional methods. The focus is on the general applicability of personalised learning, with limited attention to specific health sciences fields.

Alamri et al. (2021) conducted a mixed-methods study that explores the implementation of personalised learning environments in higher education, particularly in Saudi Arabia [7]. The authors define personalised learning as the tailoring of educational experiences to individual preferences and learning styles. The study highlights positive outcomes, such as increased student engagement and motivation, and calls for research on implementing personalised learning in low-resource settings. A meta-analysis by Bernard et al. (2014) examines the effects of blended and personalised learning on critical thinking and problem-solving skills [8]. The study defines personalised learning as an approach that uses adaptive technologies to customise instruction based on student performance. The findings indicate significant improvements in academic skills, though the study notes variability in implementation strategies. A qualitative study by Prinsloo & Slade (2017) addresses the digital divide and equity issues in personalised learning [14]. The authors define personalised learning as an adaptive educational method that seeks to meet individual student needs. The study underscores the importance of equitable access to technology and highlights challenges related to ensuring all students benefit from personalised learning approaches.

Implemented strategies of personalised learning in health sciences higher education

The review identified several key studies that explored the strategies used to implement personalised learning in health sciences higher education and their evaluation. These studies provide insights into various approaches and technologies employed to tailor educational experiences to individual student needs, as well as their effectiveness. Siemens (2013) discusses the emergence of learning analytics as a tool for personalised learning [15]. Siemens highlights the use of data-driven approaches to tailor educational experiences based on student performance and engagement. The study emphasises the importance of learning analytics in informing instructional design and providing personalised feedback. Gašević et al. (2015) conducted a quantitative study that examines the role of adaptive learning technologies in personalised learning [16]. The authors discuss how these technologies provide real-time feedback and adjust instructional content to meet individual learner needs. The study found that adaptive learning platforms significantly improved student engagement and learning outcomes.

Fidalgo-Blanco et al. (2016) explore the hybrid xMOOC/cMOOC (extended Massive Open Online Courses/connective Massive Open Online Courses) approach to personalised learning in higher education [17]. The authors highlight the use of both structured (xMOOC) and connectivist (cMOOC) models to create personalised learning pathways. The study reports positive outcomes in terms of student collaboration and knowledge construction. A systematic review by Khalil et al. (2023) focuses on learning analytics in support of inclusiveness and disabled students [18]. The authors examine various strategies for implementing personalised learning environments that cater to diverse student needs, including those with disabilities. The study emphasises the role of inclusive design and technology in enhancing learning experiences for all students. A case study by Garrison & Vaughan (2013) discusses institutional change and leadership in the context of implementing blended learning models [19]. The authors describe the use of blended learning to create personalised educational experiences, combining online and face-to-face instruction. The study highlights the importance of faculty engagement and institutional support in successful implementation.

Benefits and limitations of personalised learning in health sciences higher education

The review identified several key studies that outlined the benefits and limitations of personalised learning in the context of health sciences higher education. These studies provide a comprehensive understanding of the potential advantages of personalised learning, as well as the challenges associated with its implementation. Pane et al. (2015) examined the impact of personalised learning on student engagement, satisfaction, and academic performance [20]. The study found that personalised learning environments significantly improved these outcomes. However, it also highlighted challenges related to the scalability and implementation of the necessary technologies. Dziuban et al. (2016) discuss the benefits of personalised learning, including enhanced academic outcomes and skill development [6]. The authors emphasise the positive effects of adaptive learning technologies. The study notes the limited focus on specific health fields as a limitation.

Mixed-methods study by Alamri et al. (2021) explores the implementation of personalised learning environments in Saudi Arabian higher education [7]. The study reports that personalised learning effectively addresses diverse student needs, leading to increased engagement and motivation. However, it also points out challenges related to implementing these strategies in low-resource settings. Bernard et al. (2014) investigate the impact of blended and personalised learning on critical thinking and problem-solving skills [8]. The findings indicate significant improvements in these areas. The study, however, highlights the variability in implementation strategies as a limitation. The qualitative study by Prinsloo & Slade (2017) addresses the inclusiveness of personalised learning for disabled students [14]. The authors find that personalised learning supports inclusiveness and caters to diverse needs. The study emphasises the digital divide and equity concerns as significant limitations.

Implications of personalised learning in health sciences higher education

The review identified several key studies discussing the implications of personalised learning specifically in the context of health sciences at higher education. These studies provide insights into how personalised learning impacts educational practices, policy, and student outcomes in health sciences programs. The case study by Garrison & Vaughan (2013) explores the implications of implementing blended learning models in education including health sciences [19]. The authors emphasise the need for institutional support and faculty engagement to successfully integrate personalised learning in health sciences. They highlight that strategic planning and leadership are crucial for the effective adoption of blended learning approaches in medical, nursing, and other health-related programs. Gašević et al. (2015) examine the impact of adaptive learning technologies on students’ outcomes including health sciences [16]. The authors discuss the ethical considerations and challenges associated with using adaptive technologies in personalised learning. They emphasise the importance of balancing technological benefits with ethical considerations in data use, particularly in health sciences where patient data and privacy are critical.

Siemens (2013) discusses the role of learning analytics in personalised learning in higher education [15]. Siemens highlights that while learning analytics can significantly inform personalised instruction, they also raise concerns about student privacy and data security. The study underscores the need for addressing these privacy issues to ensure the responsible use of learning analytics, especially when handling sensitive health-related data. A systematic review by Khalil et al. (2023) focuses on the use of learning analytics to support inclusiveness for disabled students in higher education [18]. The authors discuss the importance of inclusive design in creating personalised learning environments that cater to diverse student needs. The study emphasises the need for designing inclusive learning environments to support all students effectively, particularly those with disabilities, in health sciences education.

Discussion

This scoping review aimed to explore the landscape of personalised learning in health sciences higher education, focusing on its definitions, implemented strategies, benefits, limitations, and implications. The findings reveal a complex and evolving field with significant potential to transform health sciences education, while also highlighting challenges that need to be addressed for successful implementation.

Definitions and conceptual framework

Personalised learning in health sciences higher education is a multifaceted educational approach that has been interpreted and defined in various ways by different scholars. Based on the definitions provided by Alamri et al. (2021), Bernard et al. (2014), Dziuban et al. (2016), Prinsloo & Slade (2017), and Wolf (2010), six key facets emerge as central to understanding personalised learning in this context: (1) adaptive educational approach, (2) tailoring learning experiences, activities, materials, and assessments, (3) meeting individual learner needs and preferences, (4) leveraging digital technologies and instructional methods, (5) enhancing student engagement, motivation, and skill development, and (6) addressing equity and access challenges.

Personalised learning in health sciences higher education is characterised by its adaptive approach, which tailors educational experiences to meet individual learner needs and preferences. Wolf (2010) and Dziuban et al. (2016) emphasise the importance of customization and adaptability in instruction, highlighting how technology can enable these adaptive mechanisms, particularly in online settings. Alamri et al. (2021) further stress the need to accommodate diverse learning styles, which is especially relevant in health sciences, where students’ needs vary based on future professional roles and patient populations.

The integration of digital technologies, as discussed by Dziuban et al. (2016), Bernard et al. (2014), and Zawacki-Richter et al. (2021), plays a pivotal role in facilitating personalised learning through adaptive systems and learning analytics. Personalised learning also enhances student engagement and motivation by aligning educational experiences with individual goals, as noted by Alamri et al. (2021). However, equity remains a critical concern, with Prinsloo & Slade (2017) highlighting the risk of exacerbating educational disparities if access to personalised learning opportunities is not equitably distributed. Ensuring that all students benefit from these tailored approaches is essential for preparing a diverse and competent healthcare workforce.

Considering these six facets, personalised learning in health sciences higher education can be defined as an adaptive educational approach that tailors learning experiences, activities, materials, and assessments to meet individual learner needs, preferences, and performance. It leverages digital technologies and instructional methods to create a flexible and customised learning environment that enhances student engagement, motivation, and skill development, while also addressing equity and access challenges to ensure all students benefit from these tailored educational opportunities. This consolidated definition reflects the complex and dynamic nature of personalised learning in health sciences higher education. It emphasises the importance of adaptability, technological integration, and equity, all of which are crucial for preparing future healthcare professionals who are skilled, knowledgeable and responsive to the diverse needs of the populations they serve.

The various definitions reviewed underscore the need for further empirical research in this area, particularly within the specific context of health sciences, to better understand how personalised learning can be most effectively implemented and evaluated. Moreover, the cultural perspectives highlighted by Alamri et al. (2021) and the equity concerns raised by Prinsloo & Slade (2017) suggest that personalised learning approaches must be sensitive to the socio-cultural contexts in which they are applied. This is especially important in health sciences education, where cultural competence is essential for effective healthcare delivery.

The review identified various definitions of personalised learning in the context of health sciences higher education, reflecting the multifaceted nature of this educational approach. Wolf’s comprehensive review emphasises the customization of learning activities, materials, and assessments to meet individual learner needs [4]. This definition aligns with the broader educational literature, which increasingly recognises the importance of tailoring instruction to individual students’ needs and preferences [21, 22]. However, Wolf’s study also highlights a critical gap in the literature: the lack of empirical studies specifically within health sciences education.

Dziuban et al. provide a more technology-focused definition, describing personalised learning as an educational approach that adapts to individual student needs through various technologies and instructional methods [6]. This definition reflects the growing integration of technology in education and its potential to facilitate personalised learning at scale. The recent work by Xie et al. further expands on this concept, proposing a comprehensive framework for personalised and adaptive learning in online education [23]. Their framework emphasises the importance of learner characteristics, learning context, and adaptive mechanisms in creating effective personalised learning environments.

The mixed-methods study by Alamri et al. offers a cultural perspective, exploring personalised learning environments in Saudi Arabian higher education [7]. Their definition, which emphasises tailoring educational experiences to individual preferences and learning styles, aligns with contemporary educational theories that recognise the diversity of learning approaches among students [24]. This cultural perspective is particularly relevant in health sciences education, where cultural competence is crucial for effective healthcare delivery.

Bernard et al.‘s meta-analysis provides a more specific definition, focusing on the use of adaptive technologies to customise instruction based on student performance [8]. This definition aligns with the growing trend of using learning analytics and adaptive learning systems in higher education [15]. The recent work by Zawacki-Richter et al. on artificial intelligence in higher education further explores the potential of these technologies in creating personalised learning experiences [25]. Prinsloo & Slade’s qualitative study brings attention to the equity aspects of personalised learning, defining it as an adaptive educational method that seeks to meet individual student needs [14]. Their focus on the digital divide and equity issues is crucial, especially in health sciences education where ensuring equitable access to quality education is vital for addressing global health disparities [26].

Implemented strategies

The review revealed a range of approaches used in health sciences higher education to implement personalised learning. Siemens’ discussion on learning analytics highlights the potential of data-driven approaches to tailor educational experiences [15]. This aligns with the growing trend of using big data in education to inform instructional design and provide personalised feedback [27]. The recent work by Ifenthaler and Yau further explores the potential of learning analytics and educational data mining in supporting personalised learning in higher education [28].

Gašević et al.‘s study on adaptive learning technologies provides evidence for the effectiveness of these tools in improving student engagement and learning outcomes [16]. This finding is particularly relevant to health sciences education, where engagement with complex medical concepts and procedures is crucial for developing clinical competence [29]. The recent work by Zhai et al. on adaptive learning in medical education further supports the efficacy of these approaches in health sciences contexts [30].

The hybrid xMOOC/cMOOC approach explored by Fidalgo-Blanco et al. offers an innovative strategy for personalised learning that combines structured content with collaborative knowledge construction [17]. This approach could be particularly beneficial in health sciences education, where students need to master standardised content while also developing collaborative skills essential for interprofessional practice [31]. The recent work by Nagi et al. on the application of artificial intelligence in medical education further explores how these technologies can support personalised learning in health sciences [32].

Khalil et al.‘s systematic review on learning analytics for inclusiveness brings attention to the importance of designing personalised learning environments that cater to diverse student needs, including those with disabilities [18]. This focus on inclusive design is crucial in health sciences education, where creating a diverse and inclusive healthcare workforce is a key priority [33]. The recent work by Zawacki-Richter et al. on artificial intelligence in higher education further explores how these technologies can support inclusive and personalised learning environments [25].

The case study by Garrison & Vaughan on institutional change and leadership in implementing blended learning models highlights the importance of faculty engagement and institutional support [19]. This finding is particularly relevant to health sciences education, where resistance to change and institutional inertia can be significant barriers to implementing innovative educational approaches [34]. The recent work by Ifenthaler and Yau on educational technologies and learning analytics further emphasises the importance of institutional readiness and faculty development in successfully implementing personalised learning [28].

Benefits and outcomes

The benefits of personalised learning in health sciences higher education are substantial and evident from several studies. Pane et al.‘s research demonstrates significant improvements in student engagement, satisfaction, and academic performance [20]. These outcomes are crucial in health sciences education, where high levels of engagement and academic achievement are necessary for developing clinical competence [35].

Dziuban et al.‘s study further supports these findings, emphasising the positive effects of adaptive learning technologies on academic outcomes and skill development [6]. The recent work by Xie et al. on personalised and adaptive learning in online education provides additional evidence for the efficacy of these approaches in improving learning outcomes [23].

The mixed-methods study by Alamri et al. provides evidence that personalised learning effectively addresses diverse student needs, leading to increased engagement and motivation [7]. This is particularly relevant in health sciences education, where students often come from diverse backgrounds and have varying levels of prior knowledge and experience [36].

Bernard et al.‘s meta-analysis provides strong evidence for the positive impact of blended and personalised learning on critical thinking and problem-solving skills [8]. These skills are fundamental in health sciences education, where clinical reasoning and decision-making are core competencies [37]. The recent work by Zhai et al. on adaptive learning in medical education further supports the efficacy of these approaches in developing critical clinical skills [30].

While personalized learning focuses on tailoring educational experiences to individual learners, it is equally important to consider the role of educators in this process. Educators often face significant challenges, including limited time, insufficient training, and the need for institutional support to adapt teaching methods effectively [19, 34]. Future research should explore strategies to support educators in implementing personalized learning, ensuring that both learners and instructors benefit from these approaches.

Challenges to personalised learning

Despite the numerous benefits, the review also identified several limitations and challenges associated with personalised learning in health sciences higher education. Pane et al. highlight issues related to scalability and implementation of necessary technologies [20]. These challenges are particularly acute in health sciences education, where large class sizes and the need for hands-on clinical training can complicate the implementation of personalised learning approaches [38].

Prinsloo & Slade’s study brings attention to the digital divide and equity concerns [14]. These issues are particularly relevant in health sciences education, where ensuring equitable access to quality education is crucial for addressing global health disparities [39]. The recent work by Zawacki-Richter et al. on artificial intelligence in higher education further explores these equity concerns and proposes strategies for addressing them [25].

Selwyn’s discussion on the costs and complexity of integrating adaptive technologies highlights significant challenges [21]. In the context of health sciences education, where resources are often stretched thin, these financial and logistical barriers can be particularly daunting [40]. The recent work by Ifenthaler and Yau on educational technologies and learning analytics further emphasises the need for strategic planning and resource allocation in implementing personalised learning [28].

Ethical considerations, particularly regarding data privacy and security, pose significant challenges in implementing personalised learning in health sciences education. The work by Nagi et al. on artificial intelligence in medical education highlights the need for robust ethical frameworks to guide the use of these technologies in health sciences contexts [32].

Personalized learning approaches often require significant resource investments, including technological infrastructure, educator training, and institutional support. Adaptive learning platforms, learning analytics, and other tools central to personalized learning demand substantial financial and technical resources, which may be challenging for institutions, particularly in resource-limited settings [21]. Additionally, educators face increased workload demands, as tailoring educational experiences and providing individualized feedback can be time-intensive [19]. Institutions must consider these factors when adopting personalized learning approaches and develop strategies to ensure sustainability, such as faculty development programs and phased implementation plans.

Implications and future directions

The implications of personalised learning in health sciences higher education are far-reaching. Garrison & Vaughan’s case study emphasises the need for institutional support and faculty engagement [19]. This implies that the successful implementation of personalised learning in health sciences programs requires a systemic approach, involving leadership, faculty development, and strategic planning [40].

Gašević et al.‘s examination of adaptive learning technologies raises important ethical considerations [16]. In health sciences education, where patient data and privacy are critical concerns, these ethical implications need to be carefully addressed [41]. The recent work by Zawacki-Richter et al. on artificial intelligence in higher education provides a comprehensive framework for addressing these ethical concerns [25].

Siemens’ discussion on learning analytics underscores the need for addressing privacy issues [15]. This is particularly crucial in health sciences education, where handling sensitive health-related data requires strict adherence to ethical and legal standards. The work by Ifenthaler and Yau on educational technologies and learning analytics provides guidance on developing ethical and effective learning analytics practices [28].

Khalil et al.‘s focus on inclusive design highlights the importance of creating personalised learning environments that cater to diverse student needs [18]. This has significant implications for health sciences education, where fostering a diverse and inclusive healthcare workforce is essential for addressing health disparities [42]. The recent work by Xie et al. on personalised and adaptive learning in online education provides strategies for creating inclusive personalised learning environments [23].

Selwyn’s review of technology integration emphasises the need for managing the costs and complexities associated with personalised learning [21]. This implies that health sciences programs need to develop comprehensive strategies for technology adoption and integration, balancing the benefits with the associated challenges [43]. The work by Zhai et al. on adaptive learning in medical education provides practical insights into implementing these technologies in health sciences contexts [30].

For educators, personalized learning approaches require additional training and support to effectively utilize adaptive technologies and design individualized learning experiences. Faculty development programs and institutional policies that prioritize workload management are essential to address these challenges [34, 44]. For institutions, strategic planning and leadership are critical to managing resource allocation and ensuring equitable access to personalized learning technologies, particularly in health sciences education, where the integration of clinical training adds another layer of complexity. Future research should explore cost-effective strategies and scalable models for implementing personalized learning in diverse educational contexts.

Future research should focus on developing and evaluating personalised learning strategies specifically tailored to health sciences education, with particular attention to long-term outcomes and impact on clinical practice. There is a need for more empirical studies that examine the effectiveness of personalised learning approaches in developing specific clinical competencies. Additionally, research should explore how personalised learning can be integrated with other innovative educational approaches, such as simulation-based learning and interprofessional education, to create comprehensive and effective health sciences curricula.

Limitations

The limitations of this scoping review include the relatively small number of articles specifically addressing personalised learning in health sciences higher education. While many of the included studies were drawn from general higher education contexts, their findings required careful interpretation to determine their applicability to health sciences education. Furthermore, inconsistencies in the definition and implementation of personalised learning across studies posed challenges in comparing and generalising the results. Another limitation is the rapid pace of technological advancements in this field, which may render some of the earlier studies less reflective of current capabilities and challenges. Additionally, the adoption of personalised learning is often constrained by significant resource demands, including financial, technological, and human resources—factors that may limit its feasibility, particularly in resource-limited settings.

Despite employing a systematic and comprehensive search strategy, this review may still be subject to some degree of selection bias, a challenge inherent to scoping reviews. To mitigate this, independent reviewers were involved, and adherence to established frameworks was maintained throughout the process. Finally, as the primary aim of this scoping review was to map the existing literature and identify key concepts and gaps, a formal critical appraisal of the included studies was not conducted, which may limit the depth of quality assessment.

Conclusion

In conclusion, this scoping review reveals that personalised learning holds significant promise for transforming health sciences higher education. It has the potential to enhance student engagement, improve learning outcomes, and develop critical skills necessary for healthcare professionals. However, successful implementation requires addressing challenges related to technology integration, equity, ethics, and institutional support. As the field continues to evolve, it is crucial that health sciences educators and researchers work collaboratively to develop evidence-based strategies for implementing personalised learning that are effective, ethical, and inclusive.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (15.2KB, docx)

Acknowledgements

Not applicable.

Author contributions

M.A. conceived the idea. M.A. and I.W. searched and screened the literature, and extracted the data. H.H. and M.Y. supervised the literature search, article selection and data extraction. All authors contributed to writing and reviewing the manuscript.

Funding

This scoping review has not received any internal or external funding.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

As this scoping review included publicly available published material, ethical approval is not required.

Consent for publication

N/A.

Competing interests

The authors declare no competing interests.

Abbreviations

N/A.

Clinical trial number

N/A.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Majid Ali, Email: mm.ali@sr.edu.sa.

Izyan A. Wahab, Email: izyan@um.edu.my

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (15.2KB, docx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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