Have you heard? AI is coming. Artificial intelligence (AI) is going to be a prominent feature in educational and training settings. You may have already heard that Large Language Models are upending everything we’ve known about how to teach and learn. You may have heard rumblings that Instructional Design jobs may not be needed if smart authoring tools can crank out “good enough” content and experiences. It’s enough to make even the most dedicated designers wonder how to navigate the coming perils and possibilities.
There is no doubt that “smart” tools and resources (e.g. navigation systems and recommendation engines being two common examples) are already affecting everyday life as we know it. Websites such as Product Hunt illustrate just how many new AI -empowered products are released every day. Future designers of instruction, learning and experiences will be well served to get oriented to the possibilities and develop the research and analytical skills to ensure ongoing professional success.
So, what are we really talking about? Artificial Intelligence typically refers to a broad range of applications that use of computational algorithms and techniques to mimic or replicate human intelligence and behavior. Some examples of the most common applications of AI include:
- Machine Learning: This is AI that involves using algorithms to learn from data, rather than being explicitly programmed. Machine learning is used in a wide range of applications, including image and speech recognition, natural language processing, and predictive analytics.
- Robotics: AI is used to control and program robots to perform a variety of tasks, such as manufacturing, transportation, and healthcare.
- Computer Vision: AI-based systems that can analyze and interpret visual information, such as images and videos.
- Natural Language Processing (NLP): AI-based systems that can understand, interpret, and generate human language. This is where Large Language Models live.
- Expert Systems: AI-based systems that can perform complex tasks that typically require human expertise, such as medical diagnosis, legal research, and financial analysis.
- Neural Networks: AI-based systems that attempt to simulate the structure and function of the human brain and applied in various tasks such as image classification, language models, and generative models.
- Autonomous Systems: AI-based systems that can operate independently, such as self-driving cars, drones, and virtual personal assistants.
These are just a few examples of the many different applications of AI, and new uses for the technology are being developed all the time.
Future Skills
The good news is that we can expect there will be an increasing demand for those that have the skills necessary to work with AI technologies and leverage its potential. The most important skills for learning designers to develop to make effective use of AI include:
- AI concepts and technologies: A basic understanding of the types of AI, such as machine learning and natural language processing, as well as the specific tools and algorithms used, is a necessary first step to integrate AI thinking into learning design.
- Data analysis and interpretation: As AI systems are often based on data, learning designers need to be able to effectively analyze and interpret data to inform the design and development of AI-based learning experiences.
- Instructional, learning and experience design: AI-based learning experiences need to be designed in a way that is engaging and effective for learners. Learning designers should be skilled in a broad range of human learning and design principles and practices to create AI-based learning experiences that are intuitive, engaging, user-friendly, and meet the needs of the learners.
- Evaluation and assessment: learning designers need to be able to evaluate and assess the impact of the AI on learning outcomes. This includes designing and implement formative and summative evaluations, as well as analyzing and interpreting the data collected.
- Continuous formative learning: As AI is a rapidly evolving field, learning designers need to be comfortable with continuous learning and be open to new tools and technologies that can be used to enhance the way we approach our ongoing professional development.
It’s clear that learning designers have both challenges and opportunities ahead. By embracing AI technologies, developing crucial skills, and maintaining a growth mindset, AI becomes a powerful tool to enhance our capabilities and enable more impactful, personalized, and effective learning experiences. The future of learning design is going to be about cocreation and collaboration to unlock new possibilities. As we continue to adapt and evolve alongside these technologies, we have the potential to reshape the landscape of learning in ways we’ve only begun to imagine.