As artificial intelligence (AI) rapidly reshapes the technology landscape across industries, understanding its nuances and potential applications becomes a pressing issue for us in L&D. AI literacy is no longer a luxury, but an essential skill that ensures that we can create, adapt, and implement learning initiatives and provide performance support that leverages the capabilities of AI. However, many of us lack the know-how, insights, and resources to have a good grasp of AI. To get us started, here are some key areas and practical ideas to focus on in developing AI literacy (Figure 1).
1. Fundamentals of AI
First and foremost, we must establish a robust understanding of AI’s nature, its historical context, present-day scenarios, and prevalent methodologies. This includes the origin of AI, discerning the distinctions between machine learning and AI, list the various AI techniques, as well as comprehending their applications in L&D.
Activity Idea: To ensure that everyone has a shared knowledge of AI, organize a series of “Lunch and Learn” sessions where people are introduced to AI basics. The first session could feature a documentary on the history and evolution of AI, followed by a Q&A session with an in-house AI specialist or an external guest speaker.
Resources:
- History of AI post by The Graduate School of Arts and Science, Harvard University
- 25 Best Documentaries About Artificial Intelligence
2. Data Fluency
Given that AI is data-driven, there’s a need to hone our capacity to read, interpret, manipulate, and disseminate data. This might involve validating data quality, understanding the processes to cleanse and set up data for training AI models, and/or interpreting visual data representations to extract meaningful conclusions.
Activity Idea: To provide tools and practice opportunities in handling data, offer hands-on workshops where staff can work with datasets relevant to their roles. For instance, a sales team could be trained on how to use AI-driven analytics tools to interpret customer data and forecast sales trends.
Resources:
- Kaggle, has free and open datasets to download
- OpenRefine, an open-source tool for data cleansing
- The Data Playbook by PrepareCenter.org
3. Critical Thinking and Fact-Checking
AI chatbots is known to return false results with confidence, generate misinformation, and prone to hallucination problem. For example, chatbots can return made-up case studies or incorrect references when conducting information searches. Hence, equipping ourselves with analytical and fact-checking acumen is imperative to discern such discrepancies. This ensures that we are not accepting outcomes generated by AI blindly, and to make informed discussions on how we can best use AI in L&D.
Activity Idea: Create simulation exercises or use case scenarios where learners encounter AI-generated results with potential errors and have to determine whether they will trust the results or not in order to proceed with the activity. Challenge them to critically analyze the results, identify anomalies, and come up with solutions. This could be similar to a “choose your own adventure” concept but based on AI outputs.
Resources:
- Roft, a real or fake text game (learn when text has been written by a computer or by a human)
- What are AI hallucinations and how do you prevent them? An article on how to encourage AI to stop hallucinating
4. Common AI Applications
To understand AI’s societal implications, it’s beneficial to examine more closely its common applications in multiple sectors. Examples include AI’s role in facilitating autonomous driving, chatbot-driven customer interactions in retail, predictive modeling in finance for gauging risks, or harnessing machine learning in healthcare for prognosis and tailored treatment strategies. Such exploration sheds light on AI’s possibilities and its constraints as its influence increases in our daily lives.
Activity Idea: Host an “AI in Action” week where different departments showcase how they’re utilizing AI in their operations. For instance, the HR department could demonstrate an AI-powered recruitment tool they’re piloting, while the customer service team presents a chatbot designed to handle frequent queries. If your organization has not yet adopted any AI-enabled platforms, consider partnering with another organization or organizing a field trip to visit an organization that is outside of your domain area.
Resources:
- Top 10 Real-World Applications of Generative AI, an article on how AI is being used across different domains
- Real-World Examples of Machine Learning (ML), an article by Tableau
5. AI Ethics
Conversations about AI must invariably include its ethical dimensions – addressing issues like privacy, user consents, data ownership, biases inherent in AI processes, digital integrity, and AI’s broader ramifications on society. This could encompass debates on the ethical implications of using AI for workplace monitoring and potential breaches of learner privacy. Additionally, it’s vital to instill in our organizations the significance of using AI responsibly, which could include the creation or refining of existing data ethics policy and responsible use of AI guidelines.
Activity Idea: Create ethical debate sessions or roundtable discussions centered on real-world AI ethical dilemmas. These could include topics like the fairness of AI algorithms in recruitment or the potential biases in AI-driven learning content recommendation systems. An outcome from such sessions could be a co-development of an AI ethics guideline that can be applied across the organization.
Resources:
- Ethics of Artificial Intelligence by UNESCO
- Artificial Intelligence: Examples of ethical dilemmas by UNESCO
6. AI Pedagogy
Using AI pedagogy means that we need to address the role of AI in learning and development. It challenges us to engage our staff in critical conversations on the capabilities and limitations of AI, and to know what pedagogical principles AI tools should be based on. Furthermore, AI pedagogy needs to include practical examples and hands-on experience on how people can co-create and collaborate with AI.
Activity Idea: Introduce an AI tool (e.g. a chatbot, an AI-driven content curation system, or a predictive analytics tool) that’s relevant to your organization’s learning and development context. Each group gets a task to co-create or modify content using this tool. This could involve training a chatbot to answer FAQs about a new company policy or using predictive analytics to anticipate future training needs based on historical data.
Resources:
- Futurepedia, an AI tool repository
- There’s an AI for That, another AI tool repository
7. Future of Work
As AI continues to advance, it will significantly impact the job market. It’s essential for us to remain cognizant of the competencies and skills required in an AI-dominant future, and the emergence of roles being created or displaced. An example would be understanding how AI can automate routine tasks in different industries, but also recognizing that new jobs like AI Ethicist or AI Data Analyst are being created as AI becomes more prevalent in our society.
Activity idea: Conduct an annual “Future of Work” seminar. Invite industry experts to discuss the changing job market due to AI. Incorporate workshops where teams brainstorm how their roles might evolve with AI and what skills they’ll need to adapt. This could be complemented with a showcase of emerging roles within the organization that are a direct result of AI integration.
Resources:
- AI and the Future of Work – A Cross-Disciplinary Workshop Notes, by the British Academy
- The Future of Jobs Report 2023 by the World Economic Forum
As we prepare for the future, it is urgent not just to keep pace with technological advancements but also to cultivate a well-rounded understanding of its implications. By developing AI literacy, we as L&D professionals can position ourselves at the forefront of educational and corporate innovation, ensuring that our strategies and partnership remain relevant, efficient, and attuned to the evolving demands of the modern workplace.