Why integrate AI and EI into your eLearning development and content?

The benefits include:

• Personalized learning: AI can analyze learner data to match content and methods to individual needs, pacing, and preferences. EI can create content that keeps learners motivated and engaged.
• Enhanced engagement: Emotionally intelligent AI can recognize and respond to learners’ emotions, such as frustration, confusion, dislike, and enthusiasm. AI can also adjust the difficulty of the content, encourage learners, and challenge learners.
• Improved feedback: AI can provide learners with rapid, data-driven feedback. EI enables eLearning to deliver feedback in a supportive, constructive manner, helping learners understand their progress and areas for improvement without discouraging them.
• Adaptive learning environments: AI can create learning environments that adapt based on learner interactions. Integrating EI means that the environment adapts to emotional and motivational factors and cognitive needs. The result is a holistic learning experience.
• Accessibility: AI-driven content can make eLearning accessible to learners with disabilities by providing customized assistive technologies. EI enhances this by ensuring these technologies are sensitive to all learners’ emotional and psychological needs.

Emotional intelligence and learning

Research has shown the validity of emotional intelligence. Enterprise organizations and experts in human learning have demonstrated its value in learning. The best-known reference to EI is Emotional Intelligence by Daniel Goleman; I recommend Goleman’s work for an in-depth understanding of the topic, beginning with his assertion that emotions are impulses to act. That realization directly relates to why including EI in eLearning design is critical to the benefits listed.

Integrating EI with AI does not change the fundamental process of instructional design. The integration requires the design team to do a deeper needs assessment to address learners’ emotional and cognitive needs in the resulting experiences. This integration, in turn, leads to experiences that connect learners emotionally to the content. The emotional connection captures and maintains learners’ attention, making them more likely to persist through challenges and retain what they learn.

The integration process works for technical and “soft” topics like communication, leadership, and team building. The key is empathy mapping; the steps are the same for both.

The key to the process: Empathy mapping

An empathy map is a collaborative tool to gain deeper insights into users’ needs, thoughts, emotions, and behaviors. There are various ways to create an empathy map, but the typical way is to use a large sheet of paper or newsprint. Draw a vertical line from the middle of the top of the sheet to the middle of the bottom. Then, draw a horizontal line from the middle of one side to the middle of the other side. Doing this gives you four quadrants to record needs, thoughts, behaviors, and emotions. Label the top left quadrant “What they say,” the top right quadrant “What they think,” the bottom left quadrant “What they do,” and the bottom right quadrant “What they feel.”  

“They” can refer to an individual learner, a group of individuals, or a persona.

You can also build an empathy map using sticky notes on a wall or a whiteboard, Miro, or a similar collaboration workspace.

Do actual research to gather and validate the following information about the learners, which you (or your developer group) will record in the corresponding quadrant of the map:

• What they say: Gather direct quotes or common phrases learners use about the topic. What they say helps you understand their perspective and expectations.
• What they think: These are insights into learners’ thoughts and concerns about the topic, including their motivations and apprehensions.
• What they do: Observe learners’ actions or behaviors while engaging with the content. This step includes participation in discussions, interactions with activities, and so on.
• What they feel: Understand emotional responses to the content. Are learners excited, anxious, or indifferent?

Empathy maps are handy for integrating EI and AI into eLearning development. They can help you and your development team deeply understand and create eLearning responsive to learners’ needs and experiences. This understanding is crucial to designing EI and AI systems that resonate with learners and lead to better learning outcomes. Moreover, you can update an empathy map as you collect more data about learner interactions with the content from your AI systems. The ability to update the map is excellent for continuously refining and improving your product.

Step by step

Begin with a needs analysis using your empathy map. Once you have identified the target learners’ needs, emotions, and behaviors, the process is the same for “soft” and technical topics. Begin identifying possible objectives and outcomes for the learning experience you are designing. Identifying objectives will be a fluid process as you review the cognitive and emotional components of the learning journey.

The outline that follows mainly refers to details needed when developing soft topics. For technical topics, you will be addressing both technical proficiency and emotional engagement.

For the empathy map:

• Collect feedback on technical difficulties, tools, and concepts learners frequently discuss
• Understand learners’ thoughts on the complexity and application of technical topics
• Collect feedback about learner engagement with hands-on activities, coding exercises, or technical simulations
• Identify emotional responses to technical challenges; are learners overwhelmed, curious, or determined?

When conducting your needs analysis, use empathy mapping to understand the learners’ technical skills and emotional needs. As you develop content, use AI to adapt technical content based on the learners’ proficiency levels and learning paths. Incorporate EI to make technical content more relatable and less intimidating, using examples and analogies that resonate emotionally.

Content personalization

As you develop the content, use AI to tailor exercises and activities to what you gain insight from the empathy map about the learners, including cognitive and emotional needs; incorporate EI to create emotionally engaging and supportive content, such as storytelling or scenario-based learning that resonates emotionally.

Interactive elements

Develop interactive simulations and role-playing activities that AI can adapt in real time based on learners’ responses. Use EI to ensure the interactions are empathetic and supportive, addressing the learners’ emotional states and fostering a safe learning environment.

For technical content, develop hands-on practice, technical simulations, coding exercises, or labs that AI can adjust based on learners’ progress. Incorporate EI to encourage and reduce anxiety during challenging tasks so they feel supported.

Adaptive feedback

AI should provide learners with instant, data-driven feedback on their performance. EI needs to ensure that the feedback is constructive and delivered in a supportive manner to boost learner confidence and motivation.

For technical experiences, AI can provide detailed, data-driven feedback on exercises and positively and constructively frame feedback so learners can overcome challenges without feeling discouraged.

Iterative design

Continuously refine and improve the eLearning and technical content based on data and emotional responses captured through AI and EI systems. Update empathy maps to guide iterations and ensure the content remains relevant, effective, and engaging.

Implementing AI and EI for accessible learning

AI-driven tools can make eLearning more accessible to learners with disabilities by providing assistive technologies.

Conduct thorough needs assessments to understand the learners’ specific disabilities and emotional needs. Use empathy mapping to visualize and empathize with the learners’ experiences.

Incorporate AI-powered assistive technologies

• Text-to-speech (TTS): Implement AI-driven TTS solutions that convert written content into spoken words to aid learners with visual impairments or reading difficulties.
• Speech recognition: Use AI-powered speech recognition tools that allow learners with physical disabilities to interact with the content using their voice.
• Adaptive learning systems: Develop adaptive learning platforms that adjust the difficulty level and presentation of content based on individual learner performance and preferences.
• Real-time translation and captioning: AI can be used for real-time translation and captioning, making content accessible to non-native speakers and those with hearing impairments.

Enhance with emotional intelligence

• Emotion detection: Integrate AI systems that can detect and respond to learners’ emotions through facial expressions, voice tones, or interaction patterns to provide timely support and encouragement.
• Supportive feedback: Design feedback mechanisms that are corrective and supportive. Use EI to ensure feedback is empathetic and constructive.
• Engaging content: Create emotionally engaging content by incorporating storytelling, relatable examples, and scenarios that resonate with learners’ experiences.

Design for universal access

• Apply Universal Design for Learning (UDL) principles to ensure the content is accessible to all learners, regardless of their abilities.
• Ensure that all multimedia elements have alternative text, captions, and descriptions.

Continuous improvement

• Collect and analyze how learners with disabilities interact with the content.
• Use this data to continuously refine and improve the experience, ensuring it remains accessible and emotionally supportive.

Image credit: UnitoneVector

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The challenges

Addressing challenges in using AI in instructional design is significant because responding to these requires situational awareness and sensitivity. By tackling these challenges, designers can create better user learning experiences.

Using AI in instruction presents these three challenges for designers and L&D (Learning and Development) leaders:

1. Data privacy and security: Protecting participants’ data and complying with privacy regulations is paramount.
2. Bias in AI algorithms: AI systems can inadvertently perpetuate biases in their training data, leading to unfair outcomes. Biases must be discovered and removed, or their effects minimized during design.
3. Ethical considerations: It is essential to balance AI’s benefits with ethical concerns, such as participant autonomy and transparency.

Addressing the challenges is critical to leveraging AI’s full potential while building a fair, secure, and effective learning environment. By tackling these challenges, instructional designers can create a more inclusive, efficient, and personalized learning experience for all students.

Data privacy & security

Protecting participant privacy has become a significant concern these days. What can an instructional designer, L&D staff, and L&D leadership do to protect learners’ data and comply with privacy regulations? What are the essential privacy regulations in the USA, the United Kingdom, and the European Union?

To ensure the protection of participants’ data and comply with privacy regulations, the entire L&D team, from the instructor to the chief learning officer, can take several steps:

• Understand and comply with relevant privacy laws: Depending on their location, they should familiarize themselves with the critical privacy regulations in the USA, the United Kingdom, or the European Union. Other locations may have regulations of their own.
• Implement data protection measures: To protect personal data, use encryption, access controls, and secure storage solutions as needed. Data and data collection may include personally identifiable information or replies to open-ended questions.
• Collect only necessary data: Limit data collection to what is essential for educational purposes.
• Obtain informed consent: Ensure that participants know how their data will be used and obtain their consent.
• Provide data access and correction: Allow trainees to access their data and correct any inaccuracies.
• Train staff: Educate staff on data protection practices and compliance with privacy regulations.
• Regularly review and update policies: Keep privacy policies and practices updated with the latest regulations and best practices.

Examples of important privacy regulations

United States

• Family Educational Rights and Privacy Act (FERPA): Protects the privacy of employee education records.
• Health Insurance Portability and Accountability Act (HIPAA): Protects health information privacy.

United Kingdom

• Data Protection Act 2018: Implements the General Data Protection Regulation (GDPR) and provides additional protections.
• UK GDPR: The UK’s version of the GDPR governs the processing of personal data.

European Union

• The General Data Protection Regulation (GDPR) provides comprehensive data protection and privacy for individuals.

Bias in AI algorithms

It is essential to be aware of and mitigate bias in AI algorithms. L&D leaders and instructional designers can reduce or remove biases in AI systems to ensure fair outcomes in these ways:

• Use Diverse Training Data: To reduce the risk of bias, consider diversity in content, questions, and responses, and representation of different demographics.
• Bias Detection Algorithms: Implement algorithms specifically designed to detect and mitigate bias in AI models.
• Blind Testing: Conduct blind tests to ensure the AI system makes decisions without reference to potentially biasing information (e.g., race, gender).
• Regular Audits: Perform regular audits of AI systems to identify and address biases.
• Ethical Guidelines: Establish and follow ethical AI development and deployment guidelines.
• Human Oversight: Include human oversight in AI decision-making processes to catch and correct biases.
• Transparency: Maintain transparency in AI algorithms and decision-making processes to allow for external review and accountability.

Implementing these strategies makes AI systems more equitable and fairer, leading to better learning outcomes.

Ethical considerations

Balancing the benefits of AI with ethical concerns in instructional design is essential. Key considerations include:

• Participant Autonomy: Ensure AI tools enhance rather than replace learner autonomy. As appropriate, participants should have control over their learning paths and be able to make informed decisions.
• Transparency: AI algorithms should be transparent. Learners and instructors should understand how decisions are made, the data used, and the rationale behind AI recommendations.
• Fairness: AI systems should be designed to minimize biases and ensure fair treatment of all participants, regardless of background.
• Privacy: Protecting employees’ data is paramount. AI tools should comply with data privacy laws and only collect necessary data.
• Accountability: AI decisions should be accountable. Designers should be able to explain and justify AI actions and outcomes.
• Ethical AI Development: Designers should follow ethical guidelines during AI development to ensure that the tools are used responsibly and for the benefit of participants.

Additional considerations

In addition to the three challenges described above, the following three considerations matter in successfully implementing AI in any environment. Other than identifying them, responding to the considerations is beyond the scope of this article; you can find specific guidance in related Learning Guild content.

1. Cost and Accessibility: Implementing AI can be expensive and may be inaccessible to all L&D groups.
2. Designer and Instructor Training: Designers and instructors need adequate training to effectively use AI tools and integrate them into their teaching practices.
3. Technical Knowledge: Developing and maintaining AI systems requires instructional designers and instructors to have significant technical expertise and resources.

Conclusion

By addressing all these challenges and considerations, instructional designers can successfully leverage AI’s benefits while protecting participant rights and interests.

Image credit: Moor Studio

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If only it were that simple to understand the state of AI in L&D! Informative as they are, the two studies cited in the first paragraph are only the beginning. Grammarly’s report does not segment “workers” into functions and hierarchical levels. At the same time, Taylor and Vinauskait identify several segments on the Immaturity Scale based on responses to an online survey. In both cases, data came from surveys and case studies from North and Central America, the United Kingdom, Europe, and possibly from other locations.

In other words, individual learning leaders need to obtain information from surveys and studies that are directly useful in their curriculum planning. This article attempts, in these early days, to provide some specific guidelines for AI curriculum planning in enterprise organizations.

The two reports identified in the first paragraph help to answer an important question. What can enterprise L&D teams do to improve AI fluency in their organizations?

AI fluency improvement

Enterprise L&D teams can take several steps to improve AI fluency among employees at every level, including managers and executives. The first group of steps is straightforward.

First, show a strategic commitment to AI proficiency. Integrate AI skills and tools already adopted and available into your organization’s competency frameworks to highlight their importance.

You could be surprised how many software products have added AI features. Examples (to name a few) are productivity software (Microsoft 365 and Google Workspace); customer relationship management (Salesforce and Hubspot); human resources (Workday and Talentsoft); marketing and advertising (Adobe Marketing Cloud and Hootsuite); and communication and collaboration (Slack and Zoom). Look for more under those categories in software review sites.

Those AI-enabled applications span various industries and demonstrate how AI improves efficiency, decision-making, and user experience. Develop practical, hands-on training programs that provide these real-world applications of AI. This could include workshops and project-based learning to help employees understand how to use those AI tools effectively.

Regularly update training content to keep pace with the rapidly evolving AI landscape. This ensures that employees learn the most current and relevant skills for working with AI. Encourage a culture of continuous learning by offering ongoing AI training and development programs. This could include lunchtime seminars, company blog articles, online courses, webinars, and access to AI learning resources. Communicate the benefits of AI and provide support during transitions to new AI-powered applications. Effective change management can help alleviate fears and resistance to new technologies.

Best of all, use AI to create personalized learning paths that cater to individual employee needs and development. This can make training more engaging and effective.

By implementing these strategies, L&D teams can help bridge the AI fluency gap and empower employees to harness the full potential of AI in their roles.

Why is it important to improve AI fluency among workers?

Improving AI fluency among workers matters for several reasons. Workers who understand how to use AI effectively can automate routine tasks, freeing time for more strategic and creative work. This boosts overall productivity and efficiency.

AI tools can provide valuable insights and data-driven recommendations. Employees who are fluent in AI can leverage these tools to make more informed and accurate decisions. AI can facilitate better communication and collaboration among teams. Workers comfortable with AI tools can use them to enhance teamwork and streamline workflows.

As AI becomes more integrated into business processes, companies with AI-fluent employees will have a competitive edge. They can innovate faster and adapt more quickly to market changes.

The demand for AI-related skills will only increase as AI continues to evolve. By improving AI fluency now, organizations can ensure their workforces are prepared for future technological advancements. Effective use of AI can lead to significant cost savings by optimizing processes and reducing errors. Companies can save millions in productivity by harnessing the power of AI.

Improving AI fluency is about empowering employees to harness the full potential of AI, leading to better business outcomes and a more resilient workforce.

Get started building AI fluency

Should employees be trained about AI fluency if an enterprise still needs to adopt AI? What should be included in the training?

Even if an enterprise still needs to fully adopt AI, informing employees about AI is still beneficial. This prepares the workforce for future integration and ensures they can leverage AI tools effectively when the time comes. Here’s what should be included in the training:

• Introduction to AI: Basic concepts, history, and the current state of AI technology
• AI applications: Real-world examples of how AI is used in your organization and across different industries
• AI tools and platforms: Overview of AI tools and platforms that employees might encounter
• Data literacy: Understanding data basics, including data collection, analysis, and interpretation
• Ethics and privacy: Discuss the ethical considerations, privacy concerns, and company policy related to AI
• Hands-on practice: Where feasible, provide practical demonstrations using AI tools to solve real-world problems
• Continuous learning: Encourage ongoing education and stay updated with AI advancements

By covering these basics, employees will be more comfortable with AI, even if the enterprise still needs to implement it fully. This proactive approach can significantly ease the transition when AI adoption eventually occurs.

Would addressing basic fluency concepts in corporate blogs or other communications be effective?

Addressing AI fluency topics in corporate blogs or other communications can be very effective. Employees can access the information at their convenience without disrupting their work schedules. Occasional blog posts and other less formal mentions can reinforce learning and keep AI fluency top of mind. Blogs and communications can reach a larger audience quickly and efficiently, and they’re often more cost-effective than organizing formal training sessions.

By integrating AI fluency topics into your corporate communications strategy, you can provide continuous learning without taking time away from critical business activities.

Which employees would benefit from AI fluency improvement?

• Data analysts and scientists can leverage AI to enhance data analysis, automate processes, and derive deeper insights
• Marketing and sales teams can use AI to personalize customer experiences, predict trends, and optimize campaigns
• Customer service representatives can use AI tools like chatbots and sentiment analysis to improve customer interactions and efficiency
• HR professionals can streamline recruitment, employee engagement, and performance management processes by using AI

Even employees in roles with minimal technical interaction or non-technical roles may still benefit from AI fluency. This is equally true of those employees in highly specialized fields that don’t integrate AI, like specific creative arts or niche crafts.

What is the optimum way to improve the AI fluency of senior management and executives?

• Executive workshops and seminars: Tailored sessions focusing on strategic applications of AI, its impact on business, and decision-making
• Case studies and real-world examples: Presenting successful AI implementations in similar industries to illustrate potential benefits
• Mentorship programs: Pairing executives with AI experts or consultants for personalized learning and guidance
• Interactive learning platforms: Providing access to online courses and resources that executives can use at their own pace
• AI strategy sessions: These are regular meetings to discuss AI trends, opportunities, and integration strategies within the organization

By focusing on these approaches, senior management and executives can understand AI comprehensively, enabling them to lead their organizations more effectively in the digital age.

Inclusivity considerations

While there may be employees who seem least likely to benefit from or be important to include in AI fluency planning, it is important to consider them.

AI impacts the entire organization, not just specific departments. Understanding AI can help employees see the bigger picture and how their roles fit into the strategy. In some roles, AI can streamline processes and improve productivity.

As AI continues to evolve, roles that currently seem unaffected might change. Preparing all employees ensures they can adapt to future developments. It is also important that including everyone fosters a culture of inclusivity and continuous learning, boosting morale, engagement, and productivity.

Thinking ahead

As soon as 2029, AI in some industries and occupations may advance to the point of supporting agents: applications capable of reasoning that includes planning and taking action up to the level that an organization permits. This may take longer to happen than predicted, but we are already headed there. At that point, the nature of work will change in ways that are impossible to predict. Employees at all levels will need to understand the changes and the limitations and work with AI applications as they develop. By including all employees in AI fluency planning now, organizations can ensure a more cohesive and adaptable workforce, ready to leverage AI’s full potential. 

The post Increasing AI Fluency Among Enterprise Employees, Senior Management & Executives appeared first on Learning Guild.

Skills gaps occur when there are mismatches between employees’ skills and capabilities and the skills and capabilities needed for their work. As technology and work become more complex, identifying and correcting skills gaps become essential to optimizing employee performance.

This article discusses various methods involving skills inference and predictive analytics in addition to traditional methods to pinpoint and prevent skills gaps.

Traditional methods for identifying skills gaps

Businesses have always been concerned about skills gaps and have created methods to identify them—and processes for correcting them.

One of the most straightforward methods involves collecting self-reported employee data about their skills and competencies. This method can provide valuable insights but has limitations due to potential biases and inaccuracies in self-assessments.

Managers play a pivotal role in identifying skill gaps within their teams. Through regular check-ins and performance discussions, managers can develop a more complete view of their employees’ strengths and areas for improvement. However, this method can be time-consuming and subject to managerial biases.

Analyzing performance data helps managers identify skills employees may lack based on their job performance and key performance indicators (KPIs). While this method leverages historical performance data, it may not capture the full spectrum of skills required for future job roles.

Conducting structured workshops with employees and managers is a method to facilitate a deeper understanding of skills gaps. These workshops encourage open discussions and collaborative problem-solving. However, they require significant time and resources.

Comparing your organization’s skills with industry standards and best practices can provide a broader perspective on where gaps exist. This method offers valuable benchmarks but may only sometimes align with organizational needs.

Advanced methods for identifying skills gaps

Skills inference and predictive analytics are two sides of the same coin when identifying skills gaps.

Skills inference

Skills inference is a method that leverages advanced data analytics to infer employees’ skills based on existing data sources. It involves collecting data from self-assessments, manager evaluations, and performance reviews, then developing a comprehensive skills profile for each employee.

Organizations can identify gaps by comparing these profiles with the required skills for various roles and recommend targeted training programs to address specific gaps. The primary purpose of skills inference is to automate and personalize the identification of current skills gaps, making the process more efficient and accurate.

Typical steps in the skills inference process are:

1. Data Collection: Gather employee skills data, often through self-assessments, manager evaluations, and performance reviews. Data collection can also include job descriptions and industry standards.
2. Skill Profiling: Develop comprehensive profiles of the skills required for various organizational roles. Skill profiling requires knowing what’s needed to excel in each position.
3. Matching and Analysis: Compare each employee’s existing skills with the required skills profile for their role. This analysis involves using algorithms to identify gaps in the employee’s skill set compared to the ideal skill set.
4. Recommendation and Action: Based on identified gaps, recommend specific training, courses, or experiences. Action could involve internal training programs, external courses, or on-the-job learning opportunities.

The goal is to create a targeted development plan that enhances the workforce’s capabilities.

Predictive analytics

Predictive analytics is like the crystal ball of data science. The goal is to anticipate trends and patterns based on past information. It’s all about making informed predictions to guide decision-making.

Predictive analytics uses historical data and machine learning algorithms to forecast future skill requirements. Regression analysis and pattern recognition help analysts anticipate the skills that will be essential for upcoming job roles. Predictive analytics aims to align L&D strategies with future workforce needs, ensuring employees are ready for emerging challenges. This method offers a proactive approach to skills development, enabling organizations to stay ahead of the curve.

Comparing traditional and advanced methods

While valuable, traditional methods lack precision. Skills inference and predictive analytics, on the other hand, offer more efficient and accurate identification of skills gaps through automated processes and advanced algorithms.

Advanced methods are more scalable, making them suitable for organizations of all sizes. Traditional methods may need help to keep up with the demands of larger enterprises.

Advanced methods often produce more actionable recommendations tailored to individual needs, resulting in more effective training and development programs.

Integration of skills inference and predictive analytics

The optimal approach for identifying and addressing skills gaps appears to combine skills inference and predictive analytics. This combination allows organizations to understand their current skill landscape, anticipate future needs, and prepare accordingly. By leveraging both methods, companies can create a more comprehensive and proactive approach to identifying and addressing skills gaps: Skills inference assesses the current state of play, while predictive analytics alerts the organization to future needs. This combination ensures a balanced and forward-thinking workforce development strategy.

Integrating these advanced methods into existing L&D frameworks can pay off. Organizations should start by investing in the necessary technology and data infrastructure. Clear communication and collaboration between HR, L&D, and IT departments are essential. Potential challenges, such as data quality issues and algorithm biases, should be proactively addressed to ensure the reliability and effectiveness of these advanced methods.

Examples

Case studies highlight successfully implementing skills inference and predictive analytics to identify and address skills gaps. Here are two examples:

• McKinsey & Company’s report on how companies are reskilling to address talent gaps includes insights and success metrics from various industries.
• An MIT CISR Research Briefing explores how Johnson & Johnson used AI-powered skills inference to identify gaps and guide strategic workforce planning.  

These case studies provide valuable insights into how organizations can effectively enhance workforce development strategies.

Conclusion

Identifying skills gaps within enterprise L&D requires a multifaceted approach. While traditional methods provide valuable insights, advanced techniques such as skills inference and predictive analytics offer a more efficient, accurate, and forward-looking solution. As the role of technology in workforce development continues to evolve, organizations that embrace these advanced methods will be better positioned to maintain a competitive edge and foster a highly skilled workforce.

By adopting a comprehensive strategy combining traditional and advanced methods for finding skills gaps, enterprises can ensure their employees have the skills to thrive in an ever-changing business environment. It’s time to leverage data and analytics to drive impactful and sustainable L&D initiatives.

Image credit: Fedora Chiosea

The post Addressing Skills Gaps in Enterprise L&D: A High-Level Overview appeared first on Learning Guild.

Why does personalized learning matter?

Personalized learning can target specific skill gaps and optimize performance, leading to more effective learning and professional growth. At the same time, advances in supporting technology enable scaling learning experiences that are in effect customized journeys that make each learner’s experience more meaningful and productive.

The next step will be creating curricula that can seamlessly provide the same benefits across organizations with even more enterprise diversity. This article explores the challenges and strategies that must fit such cases by adapting curriculum design to the learning environments in today’s enterprises.

Learning environments

It’s essential to consider the types of audience environments typically available to learners, how they differ, and the challenges in designing learning experiences for each.

Different business units have different skill set requirements and different numbers of employees who must improve or reskill to satisfy them. One way to approach this is by considering how to organize and administer learning experiences in various environments.

Let’s explore the strategies for meeting the challenges of the three learning environment types: synchronous, asynchronous, and hybrid

Synchronous eLearning

A synchronous eLearning environment offers real-time virtual learning where participants engage simultaneously. These may be live online classes, webinars, or virtual meetings. This environment mimics traditional classroom settings where learners interact with instructors, learning platforms, and peers in real time. The challenges for planning and coordinating synchronous learning include:

• Coordinating sessions across different time zones
• Avoiding disruption of learning
• Keeping learners engaged over extended periods with interactive content and active facilitation

Asynchronous eLearning

Learners access asynchronous eLearning materials, complete assignments, and engage with content independently without real-time interaction with peers or instructors. Such activities include listening to recorded lectures, participating in online discussion boards, and self-paced courses. Asynchronous learning offers flexibility, allowing learners to fit learning around their schedules. Challenges with asynchronous learning include:

• Learners may struggle with motivation without real-time interaction
• Delayed feedback can hinder progress
• Lack of peer interaction can make learners feel isolated

Hybrid learning

Hybrid learning environments combine synchronous and asynchronous elements, offering real-time and self-paced learning activities. Examples include flipped classrooms and blended learning

programs. Hybrid learning provides benefits in both synchronous and asynchronous learning. Challenges with hybrid learning include:

• Seamlessly integrating both modes can be complex
• Significant resources are required to develop and manage synchronous and asynchronous components
• Ensuring a consistent learning experience across both environments can be challenging

Designing for these learning environments means balancing flexibility, engagement, and accessibility, tailoring current content and methods to meet everyone’s needs and constraints.

Design challenges for each learning environment

Synchronous eLearning

• Time Zones: Use tools like World Time Buddy to schedule sessions to accommodate various time zones. Offer multiple session times or record sessions for those who cannot attend live.
• Technical Issues: Ensure robust technical support is available. Provide learners with troubleshooting guides and access to tech support before and during sessions.
• Engagement: For live sessions, incorporate interactive elements like polls, breakout rooms, and live Q&A. Use multimedia content to break up monotony and maintain interest.

Asynchronous eLearning

• Motivation: To motivate learners, implement gamification elements such as badges, leaderboards, and progress tracking. Encourage peer-to-peer interactions through discussion boards and social media groups.
• Feedback: Utilize automated feedback systems for quick responses. Schedule regular check-ins and virtual office hours for personalized feedback.
• Isolation: Create a robust online community. Facilitate peer mentoring and group projects to foster connections and support networks among learners.

Hybrid learning

• Integration: Design a cohesive curriculum that seamlessly blends synchronous and asynchronous activities. Ensure clear guidelines and schedules so learners know what to expect.
• Resource Intensive: Invest in robust learning management systems (LMS) that support both modes. Train educators in using these systems effectively to manage and deliver content.
• Consistency: Standardize critical aspects of the learning experience, such as grading rubrics and communication channels, to maintain consistency. Use templates and frameworks to ensure uniformity across different learning activities.

These strategies can help navigate the complexities of each learning environment and create compelling, engaging, and inclusive learning experiences.

Immersive learning challenges

Each of the three environments poses challenges to using immersive learning, such as  VR and AR. These modalities also offer exciting possibilities. Let’s examine challenges—and strategies for overcoming them—for each environment:

Synchronous eLearning

Challenges:

• Technical barriers: Not all learners can access VR/AR equipment  or strong  internet  connections
• Real-time interaction: Coordinating immersive experiences in real-time can be complex and requires robust support

Strategies:

• Accessibility planning: Ensure VR/AR tools are accessible to all participants, providing loaner equipment if necessary
• Technical support: Offer comprehensive tech support to troubleshoot issues quickly
• Interactive sessions: Design shorter, focused, immersive sessions to effectively maintain engagement and manage technical constraints

Asynchronous eLearning

Challenges:

• Lack of immediate feedback: Learners can’t get real-time help if they encounter issues or have questions
• Isolation: Immersive learning might feel isolating without real-time interaction with peers or instructors

Strategies:

• Self-paced modules: Create well-structured, self-paced VR/AR modules that guide  learners through the content step-by-step
• Support channels: Establish robust support channels (like forums or chat support) to provide timely assistance and feedback
• Community building: Use discussion boards or virtual meet-ups to build a sense of community among learners

Hybrid learning

Challenges:

• Integration: Seamlessly blending immersive experiences with traditional learning methods
• Resource intensive: Developing and maintaining synchronous and asynchronous  immersive learning components can be resource-heavy

Strategies:

• Cohesive design: Develop a cohesive curriculum that integrates immersive learning elements smoothly with other learning activities
• Balanced approach: Use a balanced approach to ensure learners can switch comfortably between immersive and traditional learning environments
• Consistent resources: Allocate sufficient resources for developing and maintaining immersive content, including training instructors

Each environment has its  hurdles, but with strategic planning and the right resources, immersive learning can significantly enhance educational experiences .

Intelligent tutoring & adaptive learning

Using intelligent tutoring and adaptive learning offer both strategies and challenges for meeting the learners’ needs in  each learning environment.

Synchronous eLearning

Challenges:

• Immediate adaptation: AI systems must quickly adapt to learners’ needs in real time
• Technical issues: Real-time data processing demands robust infrastructure, which can  be prone to technical glitches

Strategies:

• Real-time analytics: Use AI algorithms that process data swiftly to provide immediate  feedback and adjust learning paths dynamically
• Reliable infrastructure: Invest in high-quality infrastructure to minimize disruptions,  ensuring a smooth experience for learners

Asynchronous eLearning

Challenges:

• Delayed feedback: Learners may not receive immediate support, hindering  progress
• Self-motivation: Without real-time interaction, learners may struggle with staying  motivated and on track

Strategies:

• Automated feedback systems: Implement AI tools that provide instant, automated feedback based on learners’ inputs
• Engaging content: Use AI to personalize content and assist in creating engaging, interactive modules that maintain learner interest and motivation

Hybrid learning

Challenges:

• Consistency: Ensuring a seamless learning experience across  synchronous and asynchronous elements
• Resource allocation: Balancing resources between real-time and self-paced components

Strategies:

• Unified platform: Use a cohesive platform that integrates  synchronous and asynchronous AI-driven learning tools
• Balanced design: Allocate resources to develop both types of content equally, ensuring learners have a consistent experience regardless of the mode

Intelligent tutoring and adaptive learning offer personalized, practical strategies to enhance educational experiences in each environment. Overcoming these challenges involves  robust  technology, thoughtful design, and continuous improvement.

Build or buy?

Is it better to build the in-house platform (s) that are needed to implement the curriculum strategies you have identified, or would it be better to buy the software and planning services (including consulting) needed? Or is there a mixed strategy?

Each approach has its benefits and drawbacks, and the best choice depends on your specific context and resources. Here’s a breakdown to help you consider.

Building in-house

Pros:

• Customization: Full control over features and design tailored to your needs
• Integration: Easier to integrate with existing systems and workflows
• Flexibility: Can be modified and scaled as needs evolve

Cons:

• Cost: Upfront development costs can be high
• Time: Development can be time-consuming
• Maintenance: Requires ongoing technical support and updates

Buying software & services

Pros:

• Speed: Ready-made solutions can be implemented quickly
• Support: Vendors provide ongoing support and updates
• Expertise: Benefit from the vendor’s expertise and experience

Cons:

• Cost: This can be expensive, particularly with licensing fees
• Customization: This may not perfectly fit all your needs
• Dependence: Reliance on vendor for updates and fixes

Mixed strategy

Pros:

• Balance: Combines the benefits of both approaches
• Customization and speed: Use off-the-shelf solutions where they fit, and customize or build specific components where necessary
• Flexibility: Can adapt over time based on feedback and evolving needs

Cons:

• Complexity: Managing both in-house development and vendor relationships can be  complex
• Cost: It may still be costly, depending on the balance of build vs. buy

Ultimately, the decision depends on  your budget, timeline, technical expertise, and specific needs. A mixed strategy is often  practical, balancing speed, customization, and cost.

Resources

Creating and maintaining a comprehensive curriculum plan can be a complex and lengthy process. This article has outlined the pieces and challenges. By taking time and an analytical approach, designing a curriculum plan that satisfies immediate priorities while future-proofing the project is possible.

There are two essential resources available to you:

• Membership in the Learning Guild provides many articles and research papers. You can extend this content by attending Guild conferences and online events. An archive of past online events is also available to the Member Plus group. Conferences are a great way to build on this further by networking with other attendees. The Guild is a professional community whose members work on the challenges described in this article. Collectively, this is an irreplaceable treasure.
• Several web resources offer comprehensive collections of software reviews. These are a great help when wrestling with the “build or  buy” question.

The Learning Guild was founded in 2002 to expand and support expertise and leadership in enterprise and government online learning. You are more than welcome to join us!

The post Tailor Personalized Curricula to Your Organization’s Learning Environments appeared first on Learning Guild.

That seems like a very bold statement. Without situational awareness, many will regard it as marketing hype, but this article provides information that canhelp you understand the growing importance of situational awareness.

A coming ‘information explosion’

 According to OpenAI founder Sam Altman, evidence points to an ‘intelligence explosion.’  Altman says, “It is possible that we will have superintelligence in a few thousand days (!)”—also acknowledging that “it may take longer, but I’m confident we’ll get there.”

He forecasts outcomes of super-intelligent AI in various articles and speeches:

• AIs will be personal assistants;
• Personalized education will become the norm;
• Jobs will be changing, but they will be changing more slowly than some people expect;
• We will need unprecedented amounts of AI infrastructure (compute and energy).

Altman attributes the advances of AI to “deep learning” and effective scaling.  He has recently launched a new device intended to facilitate scaling on a global level, discussed in the linked YouTube video. If you watch that video, I predict that it will either inspire you or scare you to death, but it might explain why having situational awareness is so important.

Commenters, including “AI philosopher” David Shapiro, have identified constraints to that explosion other than intelligence, but the main ones come down to dollars and cents and scientific progress.

What are the biggest constraints?

Constraints affect the speed of change in everything from the nature of work to the quality of life and even to the nature and success of those changes. According to Shapiro, “Even with superintelligent AI, many industries will still face physical and logistical constraints. Matter, energy, time, space, and entropy remain fundamental limitations on progress.”

Change is challenging, and none of the coming changes will happen overnight. Humans add our own constraints. Aside from money and science, it seems to me that the biggest social human constraints worldwide will be:

1. Legal, political, and cultural constraints
2. Upskilling and reskilling populations
3. Taking appropriate action to deal with social constraints
4. Understanding, developing, and applying learning science
5. Becoming and remaining ethical in the treatment of people

Maybe not so fast

Potential “gaps” arise in areas where constraints exist, including:

• Humans are not emotionally or educationally prepared to work with AI
• Systems of government that make implementation difficult
• Unethical enterprise practices
• Resistance from organizations
• Funding for necessary education and support

I believe that in order for the intelligence explosion to succeed with beneficial consequences, organizations and governments will need to take care of the last four constraint gaps. L&D needs to cover the educational constraints.

All those factors will have to be dealt with over time. Situational awareness must be exercised when an intelligence explosion is discussed. The last time an explosion like this happened on this scale was in the Industrial Revolution. Situational awareness did not extend (and perhaps it has not yet extended enough) to consequences such as global warming and social and economic dysfunction, or even tragic results including the end of humankind.  Our L&D role as educators this time must be in the middle of the action.

Ready to learn more?

Here are some resources that can help to fill in the picture:

• Atlantic’s “The AI Boom Has an Expiration Date” by  Matteo Wong
• Leopold Aschenbrenner’s paper, “Situational Awareness: The Decade Ahead“
• Wes Roth’s YouTube video, “Open AI One Step Closer to Self-Improving AI” walks you through Aschenbrenner’s paper much more quickly

I believe that situational awareness is an important skill to learn and apply during the evolution of the information explosion. Whether you agree with Altman and Aschenbrenner or not, and whether or not Altman’s idea of the Worldcoin Orb scares you, take time to develop your situational awareness.

The post Mind the Gap: L&D Needs Situational Awareness in the Coming Information Explosion appeared first on Learning Guild.

Scenario-based learning immerses learners in realistic scenarios that mimic real-world challenges they might face in their roles. These learning experiences are highly relevant and relatable. SBL is active learning. Instead of passively consuming information, learners actively engage with the content by making decisions and solving problems within the scenario. This approach enhances critical thinking and decision-making skills.

SBL can be more effective when storytelling techniques create a narrative that guides learners through the scenario to maintain engagement and make the learning memorable. Learners receive immediate feedback on their decisions and learn from their mistakes. Reflection can deepen their understanding. Branching scenarios add simulated complex decision-making processes and show the outcome of various actions through interactive scenarios where learner choices lead to different outcomes.

Designing scenario-based learning

Many software and eLearning tools and platforms support scenario-based learning. I suggest using Twine, an easy-to-use open-source tool. Creating a scenario-based learning module with Twine involves designing an interactive, branching narrative that guides learners through decision-making and problem-solving processes.

Twine has become popular for creating text-based games, interactive fiction, and experimental narratives. In this article, I will show you how Twine can create a working starter for more advanced scenario-based development tools such as Articulate Storyline, Adobe Captivate, and dominKnow One.

Using Twine to create eLearning

Begin the design process by defining learning objectives that clearly specify what you want learners to be able to do by the end of the scenario. Develop scenarios to support objectives relevant to the learners’ roles and challenges, using storytelling to make the scenarios engaging. Incorporate key decision points where a learner must take a course of action. Then, offer immediate feedback about each course of action to guide learners and reinforce learning points.

To help you get started, I developed the following sample scenario strategy by using Microsoft Copilot.

Scenario

Responding to a customer complaint.

Introduction

A customer, Mr. Smith, complains about his dinner order to the manager of a casual-themed restaurant. The manager wants to resolve the issue to Mr. Smith’s satisfaction while adhering to company policy and persuading him to return to the restaurant.

Prompt sequence and restaurant policy information

Write a step-by-step scenario in which a displeased customer, Mr. Smith, approaches a restaurant manager complaining about a food order. Smith says the food is terrible and inedible, and he will not pay. The manager noticed that most of the food on Smith’s plate had been eaten. The manager’s objective is to resolve the issue to Mr. Smith’s satisfaction while adhering to company policy and attempting to persuade Smith to make return visits to the restaurant so that the manager does not lose a customer.

Company policy gives the manager authority to take one of the following options to initiate action when a customer complains about a meal order:

1. Apologize and replace the meal with a different one at no charge
2. Apologize, and at the customer’s next visit, replace the unsatisfactory meal with a different one at no charge plus a free dessert
3. Apologize and offer the customer a coupon good at the customer’s next visit for a different meal at half off the menu price plus a free dessert
4. Apologize and cancel the check for the unsatisfactory meal

In any of the above options, it’s necessary to inform the kitchen manager of the complaint for appropriate follow-up action.

AI reply

As the manager of a casual theme restaurant, handling customer complaints effectively is crucial to maintaining a positive reputation and ensuring customer satisfaction. Here are the steps I would take in this situation:

1. Listen and empathize: I would start by listening to the customer’s complaint without interrupting, showing empathy and understanding. “I’m sorry to hear you didn’t enjoy your meal. I understand how disappointing that can be.”
2. Assess the situation: I acknowledge that most of the meal has been eaten, but I still take the complaint seriously. “I see that you’ve eaten most of the meal, but I want to make sure that we address your concerns appropriately.
3. Apologize and offer a solution: “We want to make this right for you. How about we offer you a complimentary dessert or a discount on your next visit?”
4. Investigate internally: I would also make a note of the complaint to discuss with the kitchen staff to ensure the issue doesn’t happen again. “I’ll definitely look into this with our kitchen team to make sure we improve.”
5. Follow up: If the customer accepts the offer, I would follow up to ensure they are satisfied with the resolution. “Thank you for your understanding. We hope to see you again soon and provide a better experience.”

By addressing the complaint with empathy and offering a reasonable solution, I can help turn a negative experience into a positive one, potentially retaining the customer for future visits.

Handling a difficult customer complaint: Options for reasonable solutions

Option 1

Replace the meal with a different one at no charge (option: throw in a free dessert).

Positive response from Mr. Smith: “Thank you, that sounds fair. I’ll take your offer.”

Negative response from Mr. Smith: “I don’t think that’s enough. The meal was terrible, and I don’t trust another dish.”

Feedback: This option shows immediate action but might not feel like enough compensation for a very bad experience. It’s a minimal resolution.

Option 2

Provide a coupon to replace the meal at Mr. Smith’s next visit with a different meal at no charge, plus a free dessert.

Mr. Smith’s positive response: “I appreciate that. I’ll give it another try next time and enjoy the free dessert.”

Mr. Smith’s negative response: “I don’t plan on coming back. This visit was already ruined.”

Feedback: This option is great for encouraging future visits but might fall short if Mr. Smith wants immediate resolution.

Option 3

Provide a coupon to replace the meal with a different one plus a free dessert at no charge plus half off the total menu price of the next meal.

Mr. Smith’s positive response: Wow, that’s generous. I’ll take the new meal and look forward to the discount and dessert next time.

Mr. Smith’s negative response: That’s an excellent offer, but I’d prefer to have today’s bill canceled instead.

Feedback: This comprehensive solution addresses both the immediate issue and encourages future business. It balances compensation well but may not fully satisfy a customer wanting more to make up for the bad experience.

Option 4

Cancel the bill for the unsatisfactory meal.

Positive response from Mr. Smith: Thank you for understanding. I appreciate the gesture.

Negative response from Mr. Smith: While that’s good, it doesn’t make up for the bad experience.

Feedback: This ensures that Mr. Smith leaves without financial dissatisfaction. However, it sets a precedent for future complaints and might not be sustainable in the long run.

Finishing up

Refer to the flow chart to convert the options and feedback in this article to scenario-based development tools such as Articulate Storyline, Adobe Captivate, and dominKnow One. The options for solution are the ones I successfully used in dealing with unhappy customers when I was on the management team at a well-known international restaurant chain, so I know they are effective. The point of this article, however, is to demonstrate how to use Twine to develop eLearning. Set the learning objectives, analyze the behaviors required, and design an interactive scenario-based learning experience. Have fun with the technique!

 

The post Use Twine to Easily Create Engaging, Immersive Scenario-Based Learning appeared first on Learning Guild.

In Virtual Training Tools and Templates: An Action Guide to Live Online Training, Huggett has added 80 new tools in what is now almost 400 pages in length of practical, mastery-level content. In addition, the second edition of The Virtual Trainer’s Guidebook: How to Design, Deliver, and Implement Live Online Training contains even more specific guides and tips for supporting and enhancing learning in synchronous and asynchronous settings. These books are not just theoretical, but they provide practical tools and strategies that you can apply in your virtual training.

Both books have added emphasis on accessibility in the virtual classroom. Cindy shows you how to design for engagement with the latest generation of learners who have new and different expectations for learning experiences. She addresses the demanding challenges of immersive learning and the use of Artificial Intelligence. Cindy will equip and inspire you to keep up with rapid change in the science of learning and new technology. She has always experimented with new methods and incorporated what she learns about virtual learning in the solutions she delivers to her clients and students.

That approach and the resulting expertise show on every page of these new editions. You will learn what works today along with carefully curated and time-tested knowledge from successful practitioners. Nothing is missing, from the selection of technology to the design and development of content and activities, preparation for delivery, and measurement of results. In brief, both books deliver comprehensive, mastery-level content that will take your professional success to the next level, ensuring you have a deep understanding of all aspects of virtual training.

About Cindy Huggett and the Fall 2024 Learning Guild conferences

DevLearn and Learning 2024 are ideal opportunities to learn live from Cindy Huggett. She is speaking at DevLearn in Las Vegas on November 8 on “Creating Immersive Learning Space: The New Virtual Classroom.” On December 4 in Orlando, Cindy will present “Virtual Training in 2025 and Beyond: 5 Questions Every Organization Must Ask.”

Huggett is an acclaimed author of six books and presenter of many Learning Guild conference sessions on delivering learning solutions via virtual and hybrid classrooms.

Cindy has always been a top presenter at Learning Guild conferences and our live online events. You really owe it to yourself to attend the session she presents at the conference you attend, or even better, have one member of your team attend the other conference.

The post Updated Cindy Huggett Virtual Training Classics Released appeared first on Learning Guild.

This article seeks to apply some lessons from brand management to learning design at a high level. Throughout the rest of this article, it is essential to remember that the context is an autonomous, interactive learning experience. The experience is created adaptively by Gen AI or (soon enough) by agents, not by rigid scripts. It may be that an AI will choose to present prewritten texts or prerecorded videos from a content library according to the human users’ responses or questions. Still, the overall experience will be different for each user. It will be more like a conversation than a book.

Trust issues and AI

Pew Research recently found that three out of five people do not trust AI. The mistrust may be based on fear of losing their jobs, and in any case, dislike can be reinforced by the cold voice and impersonal style of some LLM models. As creative professionals, eliminating that chill is an important part of keeping your brand’s promise when delivery is carried out by what many users will likely perceive as a “robot.”

When we talk about AI’s “voice,” we’re referring to the distinct personality and style that the AI uses to communicate with its audience. This voice helps to create a consistent and recognizable brand identity, making it easier for users to connect with and trust the brand. 

Here are some aspects of what it takes for an AI to have a voice in autonomous, interactive learning experiences.

Consistency: The voice should be consistent across all design and communication modalities. This consistency helps build trust and recognition. For example, a friendly and conversational voice might use casual language and contractions, while a more formal voice might use precise language and avoid slang.

Tone: While the voice remains consistent, the tone can change depending on the context. For instance, the tone might be more serious when discussing important updates.

Personality: The voice should reflect an authentic personality and values. This could be professional, authoritative, friendly, supportive, or any other trait that aligns with your brand’s identity.

Engagement: A well-defined voice helps engage customers by making the content more relatable and enjoyable to experience. 

It may seem odd to think of eLearning as having a voice.

So, a “voice” is needed for successful communication with users, but what does that mean when the  “instructor” is a program instead of a person? How can a designer apply this “voice” concept to asynchronous learning? How could it apply to dialog with a more autonomous chatbot that does not respond from a rigid script or FAQ file? Imagine setting up a debate between a human and an AI. How can that work?

The concept of a “voice” is essential in eLearning, especially when the “instructor” is a program. This concept can be applied to asynchronous learning and autonomous chatbots. Chatbots emulate human voices, and there are some additional requirements for their use.

Natural Language Processing (NLP): Use advanced NLP techniques to ensure the chatbot understands and responds in a way that aligns with the desired voice. 

Context Awareness: The chatbot should be able to adjust its tone based on the context of the conversation. 

Personality: Give the chatbot a distinct personality that aligns with the brand or course. 

Empathy and Support: The chatbot should be able to provide emotional support and encouragement, especially in educational settings.

Adaptability: The chatbot should be able to adapt its responses based on the user’s input and emotional state. 

By applying these principles, you can harness the power of AI to create a more engaging and effective eLearning experience, whether it’s through asynchronous content or interactive chatbots. 

User Resistance and Acceptance

Research indicates that some users resist learning experiences in which AI or chatbots actively participate autonomously. This resistance can affect user acceptance, participation, trust, and learning outcomes. Here are some key findings from published research.

Perceived Usefulness and Ease of Use: If users find the chatbot helpful and easy to interact with, they are more likely to accept it.

Trust and Relational Factors: Trust is critical in accepting AI chatbots. Relational factors, such as the chatbot’s ability to build rapport with users, are also important.

Perceived Risk and Enjoyment: Perceived risk and enjoyment also play significant roles in user acceptance. Users who perceive a high risk of data privacy issues or find the chatbot interactions unenjoyable are less likely to engage with the technology. 

Summary

In summary, while AI chatbots have the potential to enhance learning experiences, their acceptance and effectiveness depend on several factors, including perceived usefulness, ease of use, trust, relational factors, perceived risk, and enjoyment. 

Personalization and building trust are essential for maintaining user engagement and achieving positive learning outcomes. The right “voice” for autonomous AI or a chatbot can enhance trust by making interactions more personal, consistent, and empathetic. Using human-like approaches such as providing examples, logical arguments, personalization, transparency, and adapting content based on feedback can further build trust and improve user engagement.

Because AI technology is moving so quickly, this article has not explored the “how.” As more agents appear (probably in the next three to four months), I will return to provide details. In the meantime, you may now have enough information to try some experiments on your own with these LLMs: 

OpenAI’s Voice Engine

Google’s Tacotron 2

Amazon Polly

Microsoft Azure Cognitive Services

The post Voice and Trust in Autonomous Learning Experiences appeared first on Learning Guild.

The factors that affect information meaning and trust have undergone significant shifts since 2016, and humanity is on the verge of further changes. In the United States, it would appear that this trend began with the election cycle of 2016; events in other parts of the world, such as climate change, pandemics, armed conflict, nutritional endangerment, population displacement, political unrest, and economic stresses, have also driven changes that affect trust. These changes are more than distractions; the erosion of trust in institutions and between generations shows up in learning.

In this article, I will consider the effect of these changes on employees at the beginning of their careers, specifically the approximately 27% to 30% of North American and European employees entering the workforce in 2025. Similar effects will appear in other parts of the world at different times and likely with varying details due to local culture and political situations.

Meaning and Trust

My main concern is the need to consider the common factors in instructional design affecting those new employees. Trust and information affect learning design, from priorities to identifying personas. When this generation enters the workforce, their characteristics will be profoundly affected by changes like work brought about by the introduction of AI and robotics. I hope that primary and secondary education for the following generations will have evolved enough to afford a smoother transition to adult working life (whatever that becomes).

The Forces of Change

In the meantime, those of us in the learning and performance field need to adjust our plans to match the requirements of working in business and employment environments, including working with AI agents and in “lights out” factories. Much of what older generations learned about business and management will fade into the sunset. Other ideas from the 1960s, such as McGregor’s Theory X and Theory Y, remain relevant in understanding and guiding management behavior. However, their application has evolved with modern workplace dynamics.

The forces acting on normative expectations for employee performance and habits, information consumption, and trust that will be (and already are) changing include:

Digital Fluency: New generations will be highly adept at navigating digital spaces. They have grown up with technology and have become proficient in using various platforms to find and share information.

Trust in Influencers: Newer generations often trust traditional news outlets less than they trust influencers and peers. The difference may be that peers and influencers share relatable content and personal experiences, making them seem more authentic and trustworthy.

Media Literacy: The newer generation has developed strong media literacy skills through constant exposure to digital content. They are more likely to question the credibility of sources and cross-check information.

Rapid Information Consumption: Practice has given the new generation the skill to scroll through information quickly. They consume more content at a faster rate and prefer bite-sized information.

Algorithm-driven Content: Social and entertainment media algorithms personalize content feeds based on user behavior, preference, and interactions. Personalization changes GenZ’s (and eventually Gen Alpha’s) exposure to content consistent with their interests and beliefs. The result is social associations commonly called “echo chambers.”

Social media platforms:  Social media profoundly impact user engagement through usage patterns. By identifying peak usage times and preferred content, these platforms can effectively push relevant content, influencing user interaction.

Collectively, these factors shape information consumption and trust granting. Consider them as you select sources and develop content.

Critical Skills

Critical thinking skills are even more important to develop in the next five to ten years. As you create a curriculum for critical thinking skills, use the known elements that create trust. The combination will increase the engagement of employees. The foundational set of skills, according to the World Economic Forum, includes:

• Analytical Thinking and Innovation
• Active Learning and Learning Strategies
• Complex problem solving
• Critical Thinking and Analysis
• Creativity, Originality, and Initiative
• Technology Use, Monitoring, and Control
• Resilience, Stress Tolerance, and Flexibility
• Emotional Intelligence
• Leadership and Social Influence
• Curiosity and Lifelong Learning

 Building a curriculum with generationally compatible, job-related content is not just a strategic approach but a necessary one that can help employees navigate the evolving job landscape and ensure that they can contribute meaningfully to their organizations. By measuring results and adjusting content and domains appropriately, instructional designers can ensure the effectiveness of their strategies.

The post Ten-year Planning: Adjust Instructional Designs to Generational Change and Work Evolution appeared first on Learning Guild.