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AI Literacy: Implications for L&D

by | Feb 5, 2024

In continuation of our exploration of the AI literacy framework for Learning and Development (L&D), as detailed in the AI Literacy Competency Framework for Learning Professionals, this post shifts focus towards practical implementation. Here, I aim to provide a curated list of actionable tactics and resources tailored for workplace application.

1. Fundamentals of AI

Implication:
L&D professionals need to focus on building foundational AI knowledge across the organization, ensuring all employees have a basic understanding of AI concepts, its language, and its impact on various sectors.


What can we do:

  • Develop bit-size AI foundational modules for employees across all departments to familiarize themselves with basic AI concepts and terminology. It could focus on providing departmental-specific or sector-specific examples and practical use cases on how AI is being deployed.

  • Collaborate with AI experts, practitioners, and researchers for guest lectures or webinars to deepen the understanding of AI’s many subfields and their applications. This could be a good way to cultivate internal expertise as well as to bridge the gap between theoretical knowledge and practical application.

  • Leverage the organization’s existing social and collaboration platforms to facilitate AI-focused discussions, disseminate updates on the latest AI developments and their implications, and create a conducive environment for knowledge sharing. This approach includes organizing employees into groups based on their tool requirements—such as content creators—to facilitate mutual support and learning. This strategy not only encourages the sharing of AI insights but also promotes mentoring opportunities among employees with similar interests and needs.


Resources:


2. Data Fluency

Implication:
L&D professionals must lead the development of data literacy skills among staff, including the ability to interpret data, understand its significance in AI applications, and make informed decisions based on data analysis.


What we can do:

  • Conduct a data literacy assessment to gauge people’s abilities, capacities, attitudes, and experiences to establish a baseline.  The readiness assessment will help identify, prioritize, and measure the impact of your initiative.

  • Host data analysis and interpretation workshops using real-world organizational data and scenarios. Focus on applying this learning to the employees’ specific job roles and tasks.

  • Collaborate with data science teams and business intelligence teams (could be both in-house and external) to provide hands-on experience with data tools, and learn how to use data to inform decision making in business practices.


Resources:

  • Data literacy assessment – Statistics Canada has a comprehensive overview on data literacy competency frameworks as well as the assessment tools used to measure them
  • “Our World in Data” has a Teaching Hub with resources for educational purposes
  • For data visualization and storytelling, Flowing Data has courses, tutorials, projects, and many beautiful and unusual examples


3. Critical Thinking and Fact-Checking

Implication:
It is essential for L&D professionals to improve our skills in critical thinking and fact-checking, especially in the context of AI, and to foster a workforce capable of discerning the quality and credibility of information with AI generated outputs.


What can we do:

  • Develop training to enhance the ability to critically assess AI information and its sources. Focus on helping employees understand the context of AI information, recognize common logical fallacies, and identify historical instances of AI information being accurately used or distorted.

  • Lead forums or panels discussing the impact of AI on information credibility. Focus on current cases, high-profile cases, and cases that are relevant to your specific sector.

  • Create a resource hub featuring materials on AI fact-checking and critical thinking. This hub should include a wide array of examples and a curated set of tools and techniques for effective fact-checking and mis-information detection, tailored to various AI scenarios.

Resources:


4. Diverse AI Use Cases

Implication:
L&D professionals need to learn about AI applications in various sectors to understand its broad implications and to inspire innovative cross-industry applications within the organization.

What can we do:

  • Curate examples of AI applications in sectors such as healthcare, education, business, finance, and other areas. Organize these examples in a digital library or a series of case study portfolios.

  • Organize a showcase, invite leaders from diverse industries who have innovate with AI and discuss the benefits and challenges of such implementations in their respective sectors.

  • Host an internal hackathon or design sprint workshop, inviting employees from different departments to collaborate on developing new AI use cases and applications relevant to their work areas.

Resources:


5. AI Ethics

Implication:
L&D professionals need to be leaders in ethical AI – guiding their organizations in responsible and equitable AI practices.


What can we do:

  • Host workshops and debates that focus on identifying and understanding the various risks associated with AI applications. This includes biases in algorithms, privacy concerns, misinformation, and job displacement risks.

  • Mentor business units and employees in ethical AI in terms of assessing specific AI implementation risks and disseminate AI ethics examples.

  • Collaborate with HR and legal teams to draft AI ethics policies, guidelines, and good practices.

Resources:


6. AI Pedagogy

Implication:
It is necessary for L&D professionals to understand how AI tools can be integrated into learning and performance support, how to design learning initiatives that leverages AI effectively, and how to evaluate AI tools critically.


What can we do:

  • Get actively involved in the AI tool selection and evaluation process. Participate as part of the pilot project or product testing team.

  • Create an AI educational tool evaluation worksheet using evidence-based matrices, and formulate critical questions to ask in guiding AI selection and implementation.

  • Curate a list of AI tools and use cases, stating their potential benefits and limitations. Aim to position L&D as thought leaders in AI for education and capable of contributing original insights and methodologies.

Resources:


7. Future of Work

Implication:
L&D professionals ought to explore how AI will impact the future of work and be better prepared in managing our evolving job roles and tasks, analyzing the changes in workforce dynamics, as well as anticipating the potential job displacement.


What can we do:

  • Propose targeted reskilling and upskilling interventions suitable for an AI-augmented work environment or an AI-human collaboration world.

  • Facilitate discussions with cross-functional teams on AI’s impact at work. This includes addressing new job categories, organizational restructuring, and policy changes.

  • In conjunction with key business units and stakeholders, develop a future-of-work strategy reflecting AI-driven changes and how L&D can support it. Identify the need for new learning models and organizational learning cultures that nurture agility, innovation, and the ability to rapidly respond to new job categories and altered organizational structures due to AI advancements.

  • Offer assistance in streamlining routine tasks—those often seen as mundane—enabling employees to dedicate more time to their substantive, impactful work. Given that these tasks frequently constitute segments of broader workflows, leveraging AI can facilitate their automation. While intricate automations may necessitate the expertise of specialists, a significant portion of these day-to-day activities can be efficiently transformed into automated sequences with the use of no-code or low-code platforms, simplifying the process for everyone involved.

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