Correction: AI-driven framework for automated competency formalization: from professional standards to adaptive learning outcomes

The of professional competency formulation and educational outcome design in higher education has emerged as a transformative approach, driven by the integration of sophisticated educational frameworks and advanced technological systems. These automated mechanisms aim to align educational curricula with dynamic industry standards, ensuring that graduates are equipped with the precise skills, knowledge, and behaviors required for success in their professional fields (Spady, 1994;Biggs, 1999;Lam & Tsui, 2016). This alignment is critical in an era where rapid technological advancements and evolving labor market demands necessitate a responsive and adaptable educational ecosystem. By leveraging automation, institutions can systematically bridge the gap between academic preparation and professional expectations, fostering graduates who are not only academically proficient but also industry-ready 4, 5, 6, 7. At the core of this transformation lies Automated Competency Mapping, a process that employs software tools to align educational objectives with professional competencies. These tools analyze industry requirements, map them to learning outcomes, and facilitate the design of curricula that are both relevant and forward-looking. Automated systems streamline the traditionally labor-intensive process of curriculum development by identifying skill gaps, integrating stakeholder feedback, and ensuring compliance with accreditation standards 5. For example, platforms such as competency management software can cross-reference job descriptions, industry certifications, and academic standards to generate tailored competency profiles. This not only enhances the efficiency of curriculum II. LITERATURE REVIEW The formulation of professional competencies and learning outcomes is a critical area of inquiry in higher education and organizational contexts, particularly as automation and artificial intelligence (AI) reshape educational and professional landscapes. Recent studies have explored how competencies, supported by advanced technologies such as generative AI, influence learning effectiveness and organizational performance. This review synthesizes key findings from contemporary research, highlighting the role of AI-driven tools in competency development and their implications for educational and professional outcomes. Reference 17 investigated the interplay between competencies, attitudes, experience, and access to generative AI in shaping employee outcomes, with a focus on managerial roles. Employing partial least squares structural equation modeling (PLS-SEM), the study analyzed data from a diverse sample of managers to assess the direct and indirect effects of these factors. The results revealed that well-developed competencies, coupled with positive attitudes toward generative AI, significantly enhanced managers' creative engagement. This, in turn, indirectly improved learning effectiveness by facilitating the integration of AI tools into organizational processes. Specifically, the study found that competencies related to critical thinking, problem-solving, and adaptability were pivotal in enabling managers to leverage AI for innovative decision-making. These findings underscore the importance of aligning competency frameworks with emerging technologies to foster both individual and organizational learning outcomes. Moreover, the study highlights the mediating role of creative engagement, suggesting that AI-supported competencies can amplify learning by encouraging experimentation and knowledge application in dynamic work environments 17. In a complementary vein, 18 explored the integration of external AI solutions into firm-wide dynamic warehousing systems, with implications for organizational competency development. Using structural equation modeling (SEM), the study examined how collaborations with external software developers enhance AI competencies, operational efficiency, and sustainability in logistics operations. Drawing on case studies from industry leaders such as Amazon, the authors demonstrated how generative AI and external innovations optimize processes like inventory management and demand forecasting. The findings indicate that external partnerships accelerate the acquisition of AI-related competencies, enabling organizations to adapt to digital supply chain demands. For instance, Amazon's use of AI-driven robotics and predictive analytics exemplifies how competency development in AI can streamline warehouse operations while reducing environmental impact through optimized resource use. The study emphasizes the strategic importance of external collaborations in building sustainable competitive advantages, particularly in industries characterized by rapid technological change 18. These insights are relevant to educational contexts, where partnerships with industry can inform curriculum design and ensure that learning outcomes reflect current technological competencies. The studies by 17, 18 converge on the critical role of AI in enhancing competencies and learning outcomes, albeit in different contexts. In educational settings, generative AI tools can support personalized learning by tailoring content and assessments to individual student needs, thereby aligning learning outcomes with professional standards 13. Similarly, in organizational settings, AI-driven systems enable employees to develop competencies that are directly applicable to their roles, enhancing both performance and innovation 8. However, these studies also highlight challenges, such as the need for positive attitudes toward AI adoption and the potential for technological disparities to exacerbate inequities in access to competency-building opportunities 12. Beyond these specific studies, the broader literature on competency-based education (CBE) provides a theoretical foundation for understanding how competencies and learning outcomes are formulated. CBE emphasizes measurable, outcome-oriented learning, where students demonstrate mastery of specific skills and knowledge 9. Automated systems, such as learning management systems (LMS) and competency mapping software, facilitate this process by aligning educational objectives with industry requirements 4. For example, tools like Skillsoft or Degreed use AI to map competencies to job roles, enabling institutions to design curricula that prepare students for the workforce 5. These systems also support continuous assessment, ensuring that learning outcomes remain relevant in rapidly evolving fields like data science or cybersecurity 16. Despite the promise of AI and automation, the literature identifies several challenges in implementing competencybased frameworks. Developing precise and adaptable learning outcomes requires balancing specificity with flexibility to accommodate diverse pedagogical and professional contexts 15. Additionally, the integration of AI tools demands significant investment in infrastructure and training, which may pose barriers for under-resourced institutions 19. Furthermore, ethical considerations, such as mitigating biases in AI algorithms, are critical to ensuring equitable competency development 12. These challenges underscore the need for robust frameworks that integrate technological innovation with inclusive and adaptable educational practices. Academic inquiries 21, 22, and 23 underscore the paramount importance of formulating educational trajectories that consider the distinctive attributes of each learner, assess their competencies, and leverage digital resources. The research 24 introduced a structured framework for the integration of generative artificial intelligence within an evaluative system in the realm of higher education. This model encompasses the phases of automated assignment generation, customization of assessment materials, as well as the analysis and interpretation of educational outcomes utilizing the large language model (LLM). The authors underscore the critical significance of tailoring assessment methodologies to align with educational objectives (learning outcomes), thereby ensuring transparency, reproducibility, and adherence to established academic standards.In summary, the literature on competencies and learning outcomes highlights the transformative potential of AI and automation in both educational and organizational settings. Studies like those by 17, 18 illustrate how AI-driven tools enhance competency development, foster innovation, and align learning outcomes with professional demands. However, the successful implementation of these technologies requires addressing attitudinal, infrastructural, and ethical challenges. By synthesizing these insights, this review provides a foundation for further research into the automated formulation of competencies and learning outcomes, particularly in the context of higher education's evolving role in preparing students for a technology-driven workforce.Building on the challenges identified in the introduction and the insights from the literature review, this study seeks to address the automation of professional competency formulation and learning outcome development in higher education. The following research questions guide the investigation: RQ1: How can AI-driven tools be utilized to automate the extraction and formalization of learning outcomes and competency requirements from unstructured textual sources, such as professional standards, training programs, and job descriptions? RQ2: How can structured competency data be leveraged to develop a hierarchical model that captures prerequisite and postrequisite relationships, thereby enabling adaptive learning pathways aligned with labor market needs? RQ3: How can competency maps generated through intelligent modeling be integrated into existing educational software platforms to facilitate rapid, responsive, and effective curriculum design that meets evolving industry demands?III. METHODS To develop a system capable of synchronizing occupational standards with educational programs and automatically generating competency statements, this study used a generative AI methodology using the GPT model. This approach was integrated to extract, formalize and refine competency requirements from unstructured textual sources such as occupational standards, training programs and job descriptions as outlined in the research questions. The methodology was designed to ensure accuracy, contextual relevance, and alignment of the generated competencies with labor market requirements.The primary tool for generating and refining competency formulations was a transformer-based large language model, specifically a GPT architecture (e.g., GPT-4 or a similar model). The GPT model was selected for its advanced natural language understanding and generation capabilities, which enable it to process complex textual inputs and produce coherent, contextually appropriate outputs 20. The model was fine-tuned to focus on competency-related tasks, such as extracting key skills and knowledge from unstructured sources and transforming them into structured competency statements. To optimize the generation process, several parameters were configured: Temperature: Set to 1.0 to balance creativity and coherence, allowing the model to generate diverse yet relevant competency formulations. Maximum Tokens: Limited to 300 tokens to ensure concise outputs suitable for competency statements, preventing overly verbose or irrelevant content. Top-p Sampling: Employed to control the probability distribution of word selection, ensuring that generated text remains focused on the input context. These parameters were iteratively adjusted during testing to optimize the quality of the generated competencies, ensuring alignment with professional standards and educational objectives.The process of generating competency formulations followed a structured algorithm, designed to extract relevant information from textual inputs and produce formalized outputs. The workflow of Algorithm 1 can be summarized as follows Algorithm 1: Input Acquisition: Collect unstructured textual data from professional standards, training programs, or job descriptions provided via user input or external databases. Preprocessing: Apply NLP techniques, such as tokenization and named entity recognition, to clean and structure the input data, identifying key terms related to skills, knowledge, and behaviors. Model Invocation: Utilize the GPT-based model to process the preprocessed input. The model is prompted with a system instruction (e.g., "Generate competency statements based on the following professional standard: input text") to guide the generation process. 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