Content area
Aim
This study aimed to develop and validate the DigiNurse e-learning program to enhance digital competence among nursing students across Taiwan, Vietnam and Indonesia. Background: As digital health tools reshape healthcare delivery, nursing students must be equipped with digital competence. However, current educational approaches in nursing programs remain fragmented, with limited integration and minimal expert-validated frameworks.
Design
A modified Delphi study was conducted to establish consensus on the content validity of four digital competence modules and their associated post-tests.
Methods
The DigiNurse program was developed in alignment with the validated domains of the Digital Competence Assessment Checklist (DCAC), which guided both content design and the generation of post-test items. Twelve experts in nursing informatics and education evaluated the modules using a 7-point Likert scale across five criteria: content alignment, clarity, relevance, engagement and assessment. Post-tests, linked directly to module learning objectives, were iteratively refined based on expert feedback, including the transformation of items into scenario- and case-based formats to target higher-order cognitive domains. Consensus was defined as ≥ 75 % agreement with quartile deviation < 0.8.
Results
High consensus (≥82 %) was achieved across all domains, with full agreement on content alignment and clarity. Refinements strengthened interactivity and assessment strategies, ensuring pedagogical rigor and learner-centered design.
Conclusions
The DigiNurse e-learning program demonstrates strong content validity, cultural adaptability and pedagogical soundness. Future implementation and learner-centered evaluation are essential to assess its effectiveness in improving digital competence among nursing students in academic and clinical environments.
1 Introduction
Digital competence is fundamental in the rapidly evolving healthcare landscape ( Martzoukou et al., 2024). As healthcare systems increasingly adopt digital tools such as electronic health records (EHRs), telehealth and artificial intelligence (AI), nurses must be proficient in secure communication, data management and ethical decision-making to ensure safe, high-quality patient care ( Cronin, 2022; Rutledge et al., 2021). Digital competence empowers nursing students to critically assess and apply digital health information, thereby strengthening evidence-based practice ( Golz et al., 2023).
Although digital health technologies are increasingly used in clinical practice, students continue to experience gaps in cybersecurity awareness, electronic documentation and interprofessional communication (
Jones et al., 2024; Mollart et al., 2023). These deficits compromise clinical decision-making, patient safety and operational efficiency, underscoring the need for structured digital competence education (
De Rezende et al., 2024; Hants et al., 2023). To address the issues, digital competence must be systematically integrated into pre-licensure nursing curricula (
Lokmic-Tomkins et al., 2021). However, current nursing programs often lack standardized and validated curricula, creating a disconnect between academic preparation and real-world digital demands (
Livesay et al., 2024; Zhao et al., 2024).
While various educational systems have initiated the integration of digital competence, efforts remain fragmented and inconsistent across countries. In Taiwan, digital competence initiatives have focused on localized care, particularly in community and long-term care settings ( Hsieh and Chen, 2017; Kuo et al., 2020). In Vietnam, although digital transformation is underway, curricular integration remains uneven, especially in rural areas with limited access to structured programs ( Dang et al., 2021). Indonesia has introduced digital literacy through national policies, but institutional implementation varies widely ( Aspihan et al., 2021; Santioso, 2024). These initiatives reflect growing recognition of the issue but lack consistent structure, standardization and expert validation. As a result, digital topics are often introduced informally or inconsistently, leaving students underprepared in key areas such as cybersecurity, AI ethics, digital communication and clinical data interpretation ( Zhao et al., 2024; Livesay et al., 2024). This educational deficiency contributes to technostress, inefficient use of digital systems and low confidence in digital clinical environments ( Hants et al., 2023; Jones et al., 2024). To address this gap, a pedagogically sound and validated program is needed to systematically build digital competence among nursing students.
A validated educational program that comprehensively reflects the dimensions of digital competence is needed to equip nursing students for the demands of modern healthcare environments. According to Adif et al. (2024), the dimensions of digital competence encompass the ability to access and evaluate digital health information, apply digital communication in secure and professional contexts, engage in critical thinking in digital learning for evidence-based clinical decision-making and maintain digital safety through cybersecurity awareness and ethical data management.
The development of digital competence requires early introduction and continuous refinement throughout pre-licensure education ( Jones et al., 2024). Embedding digital tools into nursing curricula from the outset supports critical thinking, adaptation to emerging technologies and the confidence of students ( Waight and Holley, 2020). Structured learning environments also reduce the likelihood of clinical errors and cultivate a proactive approach to digital innovation in healthcare. Therefore, nursing students must be equipped with digital competence before entering clinical practice.
In response to these needs, this study presents the development and validation of an e-learning program, DigiNurse, designed to enhance digital competence in nursing education. The study addresses existing gaps in educational structure, standardization and content validation, offering a systematic and culturally adaptable model for preparing future nurses in the digital age.
2 Methods
2.1 Study design and development process
This study represents the validation phase of a broader research project aimed at strengthening digital competence in nursing education. The Digital Competence Assessment Checklist (DCAC), developed by Adif et al. (2024), provided the conceptual foundation for the development of the DigiNurse modules. Each module was named according to the four domains of the DCAC, ensuring conceptual alignment between the instructional structure and the underlying framework. This study did not revalidate the DCAC; rather, the checklist served as a reference to align module content, learning objectives and digital competence outcomes for nursing students.
The content development phase comprised a comprehensive literature review and systematic mapping of learning objectives to instructional content, as summarized in
The development process provided the foundation for the subsequent modified Delphi approach, through which an expert panel was invited to evaluate the instructional modules and associated post-test items. Experts provided quantitative ratings via a Likert scale and qualitative feedback, both of which informed the content refinement process. Ethical approval for this study was granted by the National Cheng Kung University Human Research Ethics Committee (Approval No. NCKU HREC-E-112–720–2).
2.2 Expert panel recruitment
A modified Delphi approach was used to validate the four DigiNurse modules and the respective 10-item post-tests. Twelve experts in nursing informatics or digital education were purposively selected following content validation guidelines ( Lynn, 1986; Polit et al., 2007). To ensure balanced representation and cross-cultural relevance, four experts were recruited from each of the three participating countries: Taiwan, Vietnam and Indonesia. This sample size aligns with established standards, which recommend between 5 and 10 experts for content validity studies.
All selected experts held doctoral degrees, had more than 10 years of teaching experience in higher education and were actively engaged in curriculum development or digital health education. Experts were identified and invited through professional networks and institutional affiliations facilitated by the project’s co-principal investigators (Co-PIs) in each participating country. The expert evaluation process was conducted between October 1 to November 30, 2024.
2.3 Delphi procedure and evaluation criteria
Experts received formal invitations outlining the study aims, confidentiality provisions and participation expectations. Supporting materials included the DCAC framework, a mapping of module objectives and teaching content in video format. All evaluations were conducted online via Google Forms to facilitate access and standardization.
Experts reviewed four modules: Use of Digital Media and Resources, Application of Digital Communication, Cognition in Digital Learning and Safety in Digital Environment. Each module was evaluated using a 7-point Likert scale across five categories: content alignment, clarity-comprehensibility, relevance-applicability, engagement-interactivity and assessment-feedback ( Uys and Gwele, 2004). Items rated below 4 required qualitative justification and suggestions.
In addition to evaluating the module content, experts reviewed 10-item post-tests using a dichotomous Yes/No scale to determine alignment with learning objectives. Items marked “No” were accompanied by qualitative feedback, used to inform revisions.
Consensus was defined as at least 75 % of experts assigning a score of 5 or higher, with a quartile deviation (QD) below 0.8 ( Holden and Wedman, 1993; Rayens and Hahn, 2000; Von Der Gracht, 2012). Data were analyzed using SPSS 29 (IBM, USA). This process adhered to accepted methodological standards for Delphi content validation, supporting the rigor and reliability of the findings.
2.4 Internal thematic analysis and refinement
Following the completion of the first Delphi round, the research team initiated a structured internal evaluation process to rigorously analyze expert feedback. All qualitative responses were exported and organized by module, then subjected to inductive thematic analysis ( Braun and Clarke, 2006). Two researchers independently coded the data line by line, identifying recurring ideas and categorizing them under preliminary codes. Data were coded manually using Excel spreadsheets. These codes were reviewed and refined during weekly team meetings to reach consensus and generate overarching themes.
Themes were derived inductively to ensure that the refinement process was grounded in participant-driven insights rather than constrained by predefined categories. This analytic process ensured transparency, triangulation and methodological rigor and it informed the refinement of both module content and post-test assessments.
3 Results
A modified Delphi approach validated four e-learning modules designed to enhance digital competence among nursing students. The findings demonstrated a strong expert consensus on content alignment, clarity-appropriateness and relevance-applicability, confirming the appropriateness of the modules for nursing education.
3.1 Internal evaluation and thematic analysis by research team
Thematic analysis of expert feedback resulted in three key areas of refinement: (1) enhancing cognitive complexity, (2) integrating ethical and AI-related content, and (3) increasing the realism and practical relevance of learning activities.
3.1.1 Cognitive depth and higher-order thinking
Experts emphasized the need for assessments that challenge analytical reasoning, clinical judgment and decision-making. As a result, all post-tests were redesigned to include scenario-based and case-study-based formats that align with Bloom’s taxonomy domains, such as application, analysis and evaluation.
3.1.2 Integration of ethical and AI-related content
Given the rapidly evolving digital health landscape, experts recommended integrating issues such as AI tool evaluation, ethical decision-making in digital documentation, data privacy and plagiarism awareness. Modules were updated to reflect these priorities in both instructional content and assessment items.
3.1.3 Realism and practical relevance
Feedback underscored the value of embedding authentic, context-specific learning tasks. Real-world nursing scenarios, such as verifying data-sharing requests, managing misinformation and navigating AI-generated clinical guidance, were integrated to strengthen the digital decision-making of students in practice.
This rigorous, team-based thematic analysis served as an additional validation layer and provided the evidence base for targeted revisions, justifying the decision not to proceed with a second Delphi round ( Keeney et al., 2011; Skulmoski et al., 2007).
3.2 Module validation and content refinement
All four DigiNurse modules underwent expert validation using a five-criterion framework: content alignment, clarity, relevance, engagement and assessment design. Full consensus (100 %) was reached for content alignment, clarity and relevancy across all modules, while engagement and assessment criteria showed slightly more variability (82–91 %), especially in Modules 3 and 4. The quantitative results of the Delphi stage are presented in
3.2.1 Module 1: Use of digital media and resources
Experts affirmed the module’s focus on digital competence but recommended further development of data verification practices and responsible AI tool use. Revisions included the introduction of the RADAR framework for evaluating digital sources, improved guidance on fact-checking and database selection and interactive assessments. Supplementary materials were expanded to include a case involving a data-sharing error and ethical decision-making of nurses in the face of phishing attempts.
3.2.2 Module 2: Application of digital communication
The module was recognized for its innovative use of transmedia and focus on digital professionalism. Experts called for deeper emphasis on ethical communication, misinformation management and secure documentation. In response, additional content and assessments were introduced to address AI-assisted writing tools, intellectual property protection and secure messaging protocols. A revised case study challenged students to evaluate a viral social media post versus peer-reviewed guidelines in shaping institutional policy.
3.2.3 Module 3: Cognition in digital learning
Experts suggested replacing app-building components with more cognitively engaging activities. The module was revised to focus on synthesizing digital knowledge, using cognitive mapping and conducting reflective simulations. Post-test items were updated to assess digital decision-making and information integration in clinical scenarios. A case study presented an AI chatbot giving unsafe advice, prompting students to evaluate ethical safeguards in digital communication systems.
3.2.4 Module 4: Safety in digital environment
Module 4 addressed data protection, cybersecurity and legal compliance. Experts noted variability in engagement and assessment feedback and recommended integrating more case-based learning. Revisions included interactive simulations on data breaches, privacy legislation and patient consent. One scenario-based item asked students to identify the appropriate response when receiving a suspicious data request, reinforcing both ethical standards and institutional protocols.
3.3 Post-test assessment transformation
Informed by both quantitative ratings and qualitative feedback, all original 10-item post-tests, initially structured as knowledge-recall multiple-choice items, were substantially revised. Experts evaluated item alignment with learning objectives using a dichotomous Yes/No scale, with written feedback required for items marked “No.” Comments consistently emphasized the need to increase cognitive complexity, enhance authenticity and incorporate ethical and practical elements aligned with digital competence in contemporary healthcare.
To address these concerns, each module’s post-test was redesigned into a dual-format structure, comprising five scenario-based and five case-study-based questions. These revised items were explicitly aligned with Bloom’s higher-order cognitive domains, including application, analysis and evaluation and targeted core dimensions of digital competence relevant to nursing practice ( Adams, 2015). The revisions enhanced content validity, ensuring alignment between instructional objectives, digital health priorities and assessment strategies.
3.3.1 Module 1: Use of digital media and resources
In Module 1, which focuses on sourcing, evaluating and analyzing digital health information, a knowledge-level item about identifying credible sources was revised into a scenario where a nurse is tasked with updating hospital policy and must decide between using a peer-reviewed journal or a health blog. The revised question prompts students to apply critical thinking and digital literacy to determine source trustworthiness. Another revision replaced a factual item about data visualization with a case where a nurse must present trends in patient readmissions using an appropriate digital tool. These revisions asses the ability of students to apply data interpretation skills in clinical contexts and to critically evaluate the rigor of digital sources.
3.3.2 Module 2: Application of digital communication
In Module 2, which addresses the ethical and professional use of digital communication tools, a knowledge-based item on messaging practices was revised into a scenario where a nurse uses an unencrypted platform to transmit patient information. Students must evaluate the situation and propose communication practices that ensure confidentiality and compliance. Another revised question presents an AI-powered chatbot that delivers incorrect clinical guidance to a patient. Students are asked to identify the communication risks involved and propose safeguards for responsible implementation. These transformations emphasize ethical digital engagement and secure communication in clinical settings.
3.3.3 Module 3: Cognition in digital learning
In Module 3, which emphasizes digital learning and cognitive synthesis, a factual item on digital knowledge integration was revised into a case scenario requiring students to synthesize patient history, laboratory data and clinical guidelines to support decision-making. Another scenario presents conflicting digital inputs during a simulation, prompting students to manage cognitive overload and determine which data are most relevant for patient care. These items assess the capacity of students to navigate complex digital environments, apply strategic thinking and use digital tools to support clinical reasoning.
3.3.4 Module 4: Safety in digital environment
In Module 4, which centers on data protection, ethical digital practices and cybersecurity awareness, an original item on data protection regulations was revised into a scenario where a nurse receives a third-party data-sharing request. The student must determine the appropriate verification steps following legal and ethical standards. Another revision presents a hospital-wide data breach that prompts students to identify the correct institutional response and risk mitigation strategy. These items require students to apply knowledge of digital safety policies and demonstrate sound judgment in responding to digital threats.
Each transformation aimed to strengthen authenticity, cognitive engagement and alignment with the DigiNurse framework. The revisions enhanced the practical applicability of the assessment tools, ensuring they evaluate the preparedness of students for real-world digital health scenarios. Detailed examples of the original and revised questions are provided in Supplementary Table 1, which illustrates the alignment between each scenario or case and the intended cognitive level and learning objective.
4 Discussion
4.1 Interpretation of findings
This study suggests that expert-informed instructional design can effectively link digital competence education with the evolving demands of nursing practice. The Delphi results highlight the conceptual consistency and contextual significance of the DigiNurse program. These findings are consistent with prior research, which has highlighted the importance of digital competence frameworks in directing the development of organized educational interventions in health professions education ( Adif et al., 2024; Wharrad et al., 2021). The consensus among experts about content alignment and intelligibility demonstrates the strength of the instructional materials and the framework's applicability across various national contexts.
The moderate variation in the evaluation of engagement and assessment criteria is most likely due to the expert panel's educational diversity. In international Delphi examinations, such diversity is expected and frequently influenced by divergent educational philosophies and pedagogical approaches ( Skulmoski et al., 2007; Keeney et al., 2011). Experts from diverse locations may emphasize differing degrees of student autonomy, engagement tactics and feedback systems. These discrepancies do not signify faults in the material, but rather underscore the necessity for culturally responsive instructional design that fits varied educational traditions.
4.2 Integration of expert feedback
The qualitative element of the expert review procedure significantly enhanced the validation study. Inductive thematic analysis revealed three key areas for enhancement: elevating cognitive complexity, incorporating digital ethics and artificial intelligence (AI) considerations and integrating genuine clinical scenarios. These themes align with modern educational ideas that emphasize critical thinking, ethical reflection and contextual learning as essential components of digital competency ( Anderson and Krathwohl, 2001; Hall and Lloyd, 2018).
The inclusion of AI-related scenarios, notably in the communication and decision-making modules, tackles new ethical issues in nursing education. Previous studies have highlighted the necessity for healthcare practitioners to possess both technical and ethical competencies to effectively manage the increasing implementation of AI in clinical settings ( Sassi et al., 2024; Fnu et al., 2024). Likewise, improvements to the safety module demonstrate the worldwide necessity to bolster cybersecurity understanding and data control in healthcare education ( Puri and Gochhait, 2023; Singh et al., 2024).
The conversion of conventional multiple-choice evaluations into scenario-based and case-based formats signifies a purposeful transition to competency-based education. This transition promotes the use of theoretical information in practical situations and corresponds with Bloom's higher-order cognitive domains, such as application, analysis and evaluation ( Adams, 2015). This alignment improves instructional coherence and the validity of outcome assessment in nursing education.
4.3 Implications for health policy and clinical practice
At the policy level, the validated DigiNurse framework can inform the development of national nursing curriculum standards and accreditation requirements that explicitly incorporate digital competence. By providing a structured, evidence-based model, it enables policymakers to set minimum benchmarks for digital skills training across institutions, ensuring consistency in graduate preparedness. Adoption of this framework in accreditation and licensing criteria would help bridge current disparities in digital education quality across Taiwan, Vietnam, and Indonesia and could serve as a reference for other countries undergoing similar digital transformation in healthcare.
4.4 Contribution to scientific knowledge
This study advances the academic discussion on digital competency education by introducing a verified instructional intervention based on a previously defined framework. Despite the growing acknowledgment of digital competence as an essential skill for nurses, there is a lack of meticulously constructed and regionally verified educational frameworks. The DigiNurse program addresses this deficiency by converting theoretical frameworks into a practical, cross-culturally validated instructional system, thus reinforcing the empirical basis for forthcoming advancements in digital nursing education ( Golz et al., 2023; Lokmic-Tomkins et al., 2021).
4.5 Recommendations and future directions
DigiNurse must be implemented in the classroom and clinical training environments to fully realize the educational potential. Future studies should focus on evaluating student outcomes, including the level of digital competence, confidence in digital tool use and clinical decision-making under simulated or real-world conditions. Longitudinal research may also provide valuable insight into how structured digital competence education influences professional behavior, patient safety and technology adoption over time ( Waight and Holley, 2020; Hants et al., 2023).
Further validation across additional cultural and institutional settings would enhance the program's generalizability. Additionally, exploring faculty readiness and institutional barriers to implementation could support more effective integration of digital competence frameworks into existing nursing curricula.
4.6 Limitations
While this study received validation from multinational experts in Taiwan, Vietnam, and Indonesia, its scope remains limited to expert perspectives. Future research should expand the validation process to include a broader range of cultural and institutional settings. In addition, this study did not assess learner outcomes or educational impact directly. To determine the effectiveness of the DigiNurse program, implementation with nursing students is essential. Evaluations should include student engagement, knowledge acquisition, skill application and long-term competence development. A longitudinal design could provide meaningful insights into how digital competence education influences clinical reasoning, professional conduct and digital safety practices in real-world nursing environments.
5 Conclusion
This study presents expert-validated evidence that the DigiNurse e-learning program is a structured, pedagogically robust and culturally adaptable model for integrating digital competence into nursing education. The key contributions include confirmation of the program’s content validity, its alignment with the Digital Competence Assessment Checklist (DCAC) and its suitability for diverse educational contexts among nursing students in Indonesia, Taiwan and Vietnam. These findings highlight that a theoretically grounded and regionally validated curriculum can address critical gaps in nursing students’ competencies, particularly in cybersecurity awareness, ethical digital communication, AI-informed decision-making and clinical data interpretation.
Although the current phase did not evaluate the program’s effectiveness in improving student outcomes, its rigorous design and alignment with higher-order cognitive domains suggest strong potential to enhance digital competence, ethical reasoning and preparedness for technology-enabled clinical practice. The logical next step involves pilot testing with nursing students in these three countries to examine feasibility, followed by comprehensive implementation studies assessing educational impact and long-term effects on professional practice.
Ethical approval
Permission to conduct the study was obtained from the National Cheng Kung University Human Research Ethics Committee (Approval No NCKU HREC-E-112–720–2).
Funding statement
This research is a part of the research project titled "A Multinational e-Learning Program Promoting Digital Competence for Nursing Students and Advancing Inter-Professional Collaboration," funded by the
CRediT authorship contribution statement
Adif Shannastaniar Aisya: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Data curation, Conceptualization. Fetzer Susan Jane: Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology. Yen Miaofen: Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation, Data curation, Conceptualization. Lee Huan Fang: Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. Lin Mei Feng: Writing – review & editing, Supervision, Investigation. Hsu Yu Yun: Writing – review & editing, Validation, Supervision.
Declaration of generative AI and AI-assisted technologies in the writing process
The authors used ChatGPT (OpenAI) solely to support language refinement during the preparation of this manuscript. No generative AI or AI-assisted tools were used in the development of study ideas, data analysis, interpretation of results, or methodological design. All content was independently developed by the authors and thoroughly reviewed. The authors take full responsibility for the accuracy, integrity and originality of the final manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A Supporting information
Supplementary data associated with this article can be found in the online version at
Appendix A Supplementary material
Supplementary material
Table 1
| Learning Objective | Aligned Teaching Content |
| Module 1: Use of Digital Media and Resources | |
| (1) Perform data searching |
− Identify different types of web pages for information search. − Develop effective searching strategies. |
| (2) Use database to enter or to extract data and information for specific purpose |
− Perform data query in specific platform. − Create a matrix of retrieved data. |
| (3) Identify eligible digital resources that relate to practice and care |
− Identify the criteria of eligible sources. − Identify criteria of appropriate digital resources for references. |
| (4) Assess trustworthiness and rigor of the accessed digital resources |
− Evaluate the rigor of digital resources. − Establish trustworthiness of digital resources. − Evaluate rigor in digital resources. |
| (5) Conduct data/information Interpretation accurately |
− Understand the steps for accurate data interpretation. − Identify challenges in data interpretation. |
| (6) Use application to aggregate data in the healthcare context |
− Understand the definition of data aggregation. − Understand how to navigate the systems that include a database of health information about patients. |
| (7) Use application to perform analysis of data, including data visualization, evaluation and reporting |
− Understand digital data analysis. − Identify data visualization technique. − From Analysis to Action: Evaluating and Reporting. |
| Module 2: Application of Digital Communication | |
| (8) Identify appropriate media for digital communication (ex. Smartphone, e-books, news, website) |
− Identify the list of media convergence in digital communication (Ex: e-books, news, website). |
| (9) Understand the merge of different types of social media in digital communication |
− Organize appropriate content of digital media to communicate with others. |
| (10) Use transmedia format to communicate with users in health care context |
− Identify the appropriate transmedia format to meet communication purposes with patients/clients/colleagues). − Design content narrative in the healthcare context and transform it into several digital media. − Integrate the selected transmedia format as an intervention to improve user outcomes. |
| (11) Perform adequate navigation in digital environment |
− Demonstrate the ability to scan one's environment and shift focus as needed to salient details. |
| (12) Participate in the digital environment to support and extend learning or practice/care |
− Understand the nature of participation in the digital environment. − Participate in online meeting or discussion. − Use digital tools to support learning and practice (increase engagement). |
| (13) Identify behavior norms and regulation while using and interacting in the digital world |
− Understand basic netiquette guidelines. − Use digital resources efficiently and in a manner that does not harm them or others. − Apply intellectual properties of digital resources. − Recognize legal consequences of plagiarism. |
| (14) Perform digital data sharing |
− Demonstrate data sharing. − Perform multidisciplinary collaboration to management interoperability. |
| Module 3: Cognition in Digital Learning | |
| (15) Organize digital resources in learning |
− Synthesize digital information to answer oriented purpose. − Modify digital information to generate applicable contents. |
| (16) Apply appropriate digital media and resources as strategic for health improvements |
− Recognize the value of health care provider involvement in design, selection, implementation and evaluation of application and system in health care. − Plan digital health application system/program for specific users. − Collaborate with multidisciplinary in developing and sharing digital health application system/program for users. |
| (17) Use appropriate learning platform through technology |
− Select digital learning platform or assistive technology tools as appropriate learning needs. − Identify innovative learning strategies. − Engage in digital learning education. |
| (18) Synthesize digital contents to professional knowledge |
− Apply existing knowledge to generate new ideas in digital representation. − Create digital content for specific purpose according to disciplinary conventions. − Contribute to public knowledge domain such as public forums, review. |
| (19) Identify resources to solve technical problems |
− Identify the sources of information to find help for trouble shooting. − Use relevant knowledge for the solution of technical problem. |
| Module 4: Safety in Digital Environment | |
| (20) Understand personal data regulation and legislation |
− Aware of the key rules related to data regulation. − Aware of the existence of data protection authorities. |
| (21) Recognize the use of personal information |
− Recognize the need for privacy policy in digital media use. − Understand the rights regarding personal data. − Recognize how to share personal data carefully. |
| (22) Perform online data protection |
− Protect internet-connected devices. − Demonstrate saving backup and recovering digital resources. − Perform data store protection in a variety of mediums and formats. |
Table 2
| Comments | Actions | |
| Module 1: Use of Digital Media and Resources | ||
|
− Ensure consistency across objectives in different materials (PDF, slides). − Provide strategies for verifying data accuracy and selecting AI tools. − Enhance interactivity with real-world clinical nursing examples and exercises. − Revise assessment tools to include case-based and critical thinking-oriented questions. |
− Align objectives consistently in all teaching materials. − Offer guidance on AI tool reliability and data accuracy verification. − Make the beginning and end of the module more practical. − Introduce scenario-based MCQs and Bloom’s taxonomy to enhance critical thinking. |
|
| Module 2: Application of Digital Communication | ||
|
− Expand content to include AI tool selection and secure communication strategies. − Clarify the role of nursing in multidisciplinary digital collaboration. |
− Teach strategies for selecting AI tools tailored to communication needs. − Highlight the nursing role in digital health collaborations. |
|
| Module 3: Cognition in Digital Learning | ||
|
− Increase student engagement with activities like simulations, pre/post-tests and case studies. − Address ethical application and plagiarism through practical scenarios. |
− Add reflective assessments, peer reviews and digital portfolio projects. − Integrate real-world case studies, interactive exercises and plagiarism detection tools to reinforce ethical application and prevent misconduct in digital nursing education. − Provide supplementary materials on interactive simulations, knowledge synthesis tools and collaborative peer projects to help students integrate and apply learned concepts, fostering the synthesis of clinical and digital knowledge. |
|
| Module 4: Safety in Digital Environment | ||
|
− Add practical elements such as case studies, interactive activities and updated guidance on emerging issues like phishing and cloud storage. − Simplify or elaborate on complex terminology for better understanding. − Enhance interactivity and engagement through quizzes, scenario-based activities and hands-on exercises. − Develop more reflective and practical assessment methods, including scenario-based MCQs. |
− Add case studies for practice on data protection principles. − Provide definitions and examples for complex terms like "key rules of data regulation." − Update content with guidance on self-updating practices and phishing email identification. − Incorporate quizzes, interactive videos and scenario-based learning. − Revise the 10-item questionnaire to include real-world scenarios and increase difficulty. |
Table 3
| Evaluation Criteria | Content Alignment | Clarity and Comprehensibility | Relevance and Applicability | Engagement and Interactivity | Assessment and Feedback | |||||
| Consensus | QD | Consensus | QD | Consensus | QD | Consensus | QD | Consensus | QD | |
| Module 1: Use of Digital Media and Resources | 100 % | 0.5 | 100 % | 0.5 | 100 % | 0.5 | 100 % | 0.5 | 91 % | 0.75 |
| Module 2: Application of Digital Communication | 100 % | 0.5 | 100 % | 0.5 | 91 % | 0.5 | 91 % | 1 | 82 % | 1 |
| Module 3: Cognition in Digital Learning | 100 % | 0.5 | 100 % | 0.5 | 91 % | 0.5 | 91 % | 0.5 | 91 % | 1 |
| Module 4: Safety in Digital Environment | 100 % | 0.5 | 100 % | 0.5 | 100 % | 0.5 | 82 % | 1.25 | 82 % | 1.25 |
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