A transformative age in instructional design for corporate training and professional development is about to start with the integration of Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS). This study intends to research, clarify, and explain the benefits and methods for improving instructional design using AI technology, with a focus on creating more personalized and effective training experiences (Chaivisit et al., 2024). The project investigates how AI may improve traditional educational models like ADDIE by incorporating AI and ITS to construct flexible learning environments tailored to each unique individual (Cabestrero et al., 2018; Niculescu, 2016).
AI can be used to assess learning requirements, personalize curriculum, and enhance employee knowledge management and transfer. According to Chen et al. (2023) and Maity (2019), these applications suggest that AI will help instructional designers in the future and act as a dynamic agent to promote corporate training and development. Furthermore, by minimizing biases and using data to generate personalized learning experiences, AI offers a path to more equitable and effective training solutions.
The main drive for this research is the ongoing need for corporations to maintain their competitive advantage through workforce development (Arthur et al., 2003). Although AI has been regarded as having potential, there has not been enough research done yet to support how it affects the effectiveness and efficiency of corporate training programs (Booker, 2023). To address this knowledge gap, the proposed study will examine the literature on the application of AI in corporations and industries and explore potential applications for individualized, adaptable, and engaging instructional designs (Guan et al., 2020; Yildirim et al., 2021).
AI is rapidly expanding, enhancing efficiency across industries, from warehouses to virtual hospital assistants (Yildirim et al., 2021). While AI improves many fields, research on its role in instructional design for corporate training remains limited. This review explores how AI and machine learning tools shape instructional materials in education, industry, and business (Guan et al., 2020) and whether they create individualized, adaptable, and engaging learning experiences (Yildirim et al., 2021), offering insights for future research.
The training planning process integrates learning objectives, instructional strategies, and methodologies tailored to trainees (Bell et al., 2017). In today’s fast-evolving business landscape, organizations emphasize personalized and adaptable training to keep employees competitive amid rapid technological advancements. The goal of training and development is to enhance employees’ current and future skills through targeted instruction that addresses industry-specific challenges (LiT et al., 2019).
With AI’s growing influence, there is an opportunity to refine training methodologies. AI-driven training follows four key phases: building knowledge bases, identifying training needs, organizing instruction, and providing feedback, each contributing to enhanced learning outcomes (Chen et al., 2023). As AI continues to shape professional development, exploring its impact on training effectiveness and adaptability remains crucial.
AI-driven tools, like IBM’s Watson, enhance HR decision-making by reducing bias and optimizing recruitment, training, and employee evaluation (Maity, 2019). AI streamlines hiring, automates interview scheduling, and provides instant feedback, improving efficiency. Maity (2019) specifies how AI personalizes learning in training and development:
Assessing employee performance to identify skill gaps,
Adapting training to individual learning styles,
Matching trainees with optimal trainers,
Customizing training duration and delivery,
Eliminating bias in training selection, and
Tracking progress and training effectiveness
By automating training needs assessments, content personalization, and trainer-trainee pairing, AI allows trainers to focus on strategy and workforce development, making corporate training more efficient and engaging.
Training has evolved from simple upskilling to personalized learning using models like ADDIE and Dick & Carey (Kruse, 2009). While effective, these models require significant effort. AI enhances training efficiency by identifying needs, mapping trainers, and designing programs based on data-driven insights (Maity, 2019).
AI’s limited memory systems leverage research findings and organizational data to optimize training schedules and delivery (Chen et al., 2023). These systems play a crucial role in professional training and development, ensuring tailored and data-informed learning experiences.
AI enhances training needs analysis by assessing performance gaps and identifying skill deficiencies (Maity, 2019). Acting as a virtual mentor, AI personalizes training by analyzing trainee traits and recommending tailored solutions (Maity, 2019). AI-powered tools can conduct needs assessments through simulated conversations, interpreting facial expressions, tone, and performance data. Limited-memory AI integrates motivation, self-efficacy, trainer style, and learning environment to optimize learning transfer (Burke & Hutchins, 2008; Ford et al., 2017). Additionally, AI monitors trainee progress, adjusts pacing, and modifies training programs to enhance learning outcomes (Ford et al., 2017).
KM facilitates the transfer of expertise from experienced employees to new hires, especially in organizations with ageing staff and limited training investments (Liebowitz, 2001). Effective KM requires structured documentation, accessibility, and continuous updates (Maity, 2019). AI enhances KM by extracting, organizing, and sharing expertise through data mining and knowledge discovery (Chen et al., 2023). AI-driven techniques identify patterns, structure knowledge bases, and integrate new information for improved learning (Liebowitz, 2001). Additionally, intelligent agents automate knowledge retrieval, analyzing emails, web pages, and online communities to generate new, interconnected insights (Chen et al., 2023).
AI leverages big data analysis to create personalized employee training, adapting to job demands, cognitive levels, and company needs (Chen et al., 2023). By continuously analyzing behavioral data, AI refines learner profiles and tailors training strategies. Through natural language processing, AI extracts key concepts from learner inputs, enabling businesses to design targeted courses. It also anticipates skill gaps, delivering timely knowledge for real-time learning. HR departments can integrate external market data to track evolving skill demands, allowing AI-driven training systems to function as personal mentors, enhancing workforce development and productivity (Baker, 2019; Iqbal, 2018).
Personalized Trainer: AI predicts training transfer efficiency based on trainer traits and learning environments, recommending AI-based trainers for specific trainee groups (Chen et al., 2023).
Personalized Training: AI-driven adaptive learning tailors courses, behaviors, and content, analyzing worker habits and learning outcomes to suggest relevant courses and optimize knowledge retention (Zhi, 2019). AI also marks key learning points, ensuring concise, targeted instruction (Chen et al., 2023).
This project leverages databases from the OSU library to collect and analyze data from academic publications and peer-reviewed articles published between 2000 and 2023 under a strict methodological framework. The research seeks to synthesize current knowledge on AI, ITS, and instructional design as well as identify areas where AI can significantly improve training results.
The main research objective is to investigate how AI can assist Instructional Design in corporate training and provide personalized learning experiences for employees. To achieve this objective, the following research questions were answered:
RQ1. How can AI assist instructional designers in leveraging more personalized learning materials for employees?
The research's unit of analysis is "Instructional Design Models for Corporate Training and Professional Development and Integrating Artificial Intelligence." The articles were gathered by looking for peer-reviewed articles in the OSU library database. These search criteria were applied to find these articles:
Searchfield: (TX“Instructional Design Models”) AND (Corporate Training and Professional Development) AND (Artificial Intelligence) AND (Personalized Learning)
Limit to: Full Text AND Peer Review
Publication Date: 2000 to 2023
Document Type: Journal Article
Language: English
The initial search on the Oklahoma State Library database yielded 150 articles for this study; 35 were selected from the 150 retrieved articles. A brief analysis of the abstracts was conducted, and 12 articles were selected. Further review of the selected articles was performed, and the selections were narrowed down to 8 articles. Additional articles were identified and included in this study. In total, 24 articles were identified using the OSU library database and were used in writing this review.
Our literature review reveals significant insights. Key findings from the review on the potential of AI to revolutionize instructional design in corporate training and professional development include:
Personalization and Adaptability: AI can revolutionize corporate training by providing employees with personalized and adaptable possibilities for instruction (Chen et al., 2023; Maity, 2019). This involves deploying virtual assistants, intelligent tutoring systems, and adaptable e-learning platforms designed to cater to the individual needs of every employee (Chaivisit et al., 2024).
Addressing Diverse Learning Needs: The study highlights how challenging it can be to promise fair learning outcomes for staff members with a diverse range of motivations, personalities, paces, and learning preferences. Because AI can assess performance data in real-time (Chaivisit et al., 2024), it may identify specific skill deficiencies and provide training interventions that are both targeted and efficient (Maity, 2019).
Data-Driven Diagnostics: Extensive data on employee efficiency, engagement, and collaboration can be processed by sophisticated machine learning algorithms, which can provide comprehensive diagnostics for individual development (Ford et al., 2017). This makes it possible for AI tutoring systems to adjust training parameters, such as length, format, and content, to meet the needs and preferences of each trainee.
Real-Time Personalization: The ability of AI to act as a "personal mentor" for every individual, adapting in real-time in response to feedback and progression (Navaie et al., 2024), addresses the limitations of human instructors in providing highly personalized training programs in large corporations (Ford et al., 2017; Maity, 2019).
Bias Reduction and Knowledge Management: AI's objective analysis can reduce biases in training program allocation and suggestions, providing decisions based on real performance data (Maity, 2019). Moreover, AI's data mining skills can help new hires learn from retiring staff, improving the corporate knowledge management system (Chen, 2023; Liebowitz, 2001).
The possibility of integrating emotional intelligence (EI) into AI-based training systems is one area of anticipated research focus. The capacity of humans to express and comprehend emotions remains unmatched, even with the improvements achieved by AI. We might significantly enhance user engagement and the personalization of the learning process by providing AI the ability to interpret facial expressions, body language, speech tones, and other emotional signals more precisely. The integration of AI's analytical skills with human trainers' emotional and empathetic intelligence may open the door to a workforce development strategy, providing more flexible and efficient options for training.
Integrating AI into instructional design can trigger a bright future regarding professional development (Panwar et al., 2025) and corporate training. AI can bring along personalization, adaptability, and bias reduction capacities to help instructional designers create more productive, equitable, and engaging learning environments. This idea proposes a deliberate investment in AI technology to enable a learner-centered, data-driven approach to corporate training. It highlights how important it is to integrate human expertise with AI's advantages to get the most efficient learning results.
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