The benefits and risks of using AI in learning design

Published by Cat Oxley on

abstract representation of AI. Image of digital data and information

The benefits and risks of using AI in learning design

Artificial Intelligence (AI) is a hot topic these days, receiving tremendous attention from many industries. However, AI isn’t new! In the early 1960s a computer program was successfully created that interacted with humans – ELIZA. Today, these powerful tools are seeping into all areas of our personal and professional lives.

“AI can ensure that the employees are more focused by developing a personalized experience to better hold their interest and make them feel more connected to their workplace. It will also be used more frequently as virtual mentors in the coming years, using experiential learning to improve employee comprehension and retention and create the ideal learning experience to meet business and performance objectives.”

abstract representation of AI. Image of digital data and information

AI vs ML. What’s the difference?

AI replicates human intelligence in machines, analysing data, recognising patterns, making decisions, and solving problems. Algorithms and models process information and allow AI to learn over time, improving its performance.

Machine Learning (ML) is a subset of AI that is commonly used in digital learning. It analyses data, like learning preferences and past performance, and suggests tailored learning materials for more effective and engaging learning experiences, which are more likely to be engaging and resonate better with learners.

The benefits of AI in learning design.

The use of AI is transforming learning design and is expected to further evolve with advancements in technology. So, what are the benefits of AI in learning design? Here are some of the key ones:

. Data-driven insights and improved learner experience.

In the ‘Age of Big Data’, almost every industry, including digital learning, has access to vast amounts of data. Thanks to machine learning we can benefit from this data. ML can predict future learning outcomes and the needs of learners, making it a key tool in learning design.

. Accessibility and inclusion.

AI can enhance accessibility and inclusivity in learning design. For example, AI-powered transcription services can provide real-time captioning and translation, making content more accessible to learners with hearing impairments or language barriers. AI can also support those with special educational needs, providing adaptive instruction and personalised learning pathways, leading to improved engagement and better learning outcomes.

Using AI in learning design.

“Artificial intelligence is most effective when humans retain oversight and direction. AI should be seen as a tool to enhance human capabilities rather than replace them altogether.”

Using AI can be a beneficial resource for enhancing productivity and efficiency throughout the learning design process. Collaborating with AI helps maintain a balance between automation and people’s emotional intelligence and creativity. Here are some of the ways AI can be used in learning design:

. Knowledge and content curation.

This is the process of organising and presenting relevant information in a concise and accessible way to meet learners’ needs.

. Natural language processing.

Natural language processing (NLP) combines linguistics, computer science, and AI to analyse and process natural language data in documents, enabling accurate extraction of information and organisation of documents.

. Chatbots.

Personalised support and guidance provided by AI chatbots like ChatGBT can enhance learner performance.

. Nudge learning.

Nudge learning refers to a pedagogical approach that uses subtle, timely, and contextually relevant interventions (nudges) to guide learners toward desired behaviors, such as engagement, completion of tasks, or skill development. These nudges can be delivered through various channels, such as notifications, reminders, recommendations, or personalized content, often leveraging technology and data analytics. The goal is to create a more effective and engaging learning experience by gently encouraging learners to take specific actions or make progress in their learning journey.

. Data visualisation.

AI can be used to translate information into a visual context, such as a map or graph, making it easier for the learner to understand.

Abstract image of digital data and information to represent AI in learning
The challenges of using AI in learning design.

“AI technology brings major benefits in many areas, but without the ethical guardrails, it risks reproducing real world biases and discrimination, fuelling divisions and threatening fundamental human rights and freedoms.” Ethics of Artificial Intelligence | UNESCO

While the use of AI in learning design holds great promise, there are several challenges that need to be addressed for its successful implementation. Here are some of the key challenges:

. Ethical considerations.

Ethical AI in learning design involves ensuring fairness, transparency, and data privacy. It demands responsible use of technology, unbiased content, and fostering digital well-being for equitable and safe education.

. Legal considerations.

When using AI-generated content, it’s important to consider who owns the ideas and information. Creators have the right to benefit from their own work through intellectual property rights. To avoid ownership disputes, make sure you have the necessary rights to use the content before starting any creative work.

. Accessibility and inclusion.

While AI has the potential to enhance accessibility and inclusivity, there is a risk of creating a digital divide if not implemented with fairness in mind. Ensuring fair access to AI-powered educational tools and addressing barriers, such as infrastructure limitations and socioeconomic disparities, is crucial for avoiding inequalities in learning design.

. Adaptability and contextual understanding.

It can be challenging for AI systems to comprehend the context and subtleties of various learning environments, such as cultural, linguistic, or educational differences. To effectively understand and adapt to diverse learning settings, developing AI systems, and how we use them in learning design, requires careful consideration.

. Bias and fairness.

If the data AI systems are trained on is biased or reflects societal biases, AI algorithms can perpetuate and amplify those biases, leading to unfair outcomes. To ensure fairness, learning data must be carefully curated and pre-processed to mitigate bias in AI-driven learning design.

. Content quality.

High-quality content needs to be aligned with learning objectives. As Learning Designers, we must possess the necessary technical and pedagogical skills to design, implement, and monitor how we use AI.

In conclusion, AI offers a wealth of benefits to support design and creativity in the realm of learning design. While AI is not new, its capabilities are continually advancing. So, what transformative potential does an ‘intelligent’ future hold for your business?  

We work with a range of forward-thinking organisations that are either already using AI to support their learners or are in the process of exploring the possibilities. Our team of designers, writers and developers work closely with our in-house team of behavioural science researchers and consultants to keep up to date with the latest research and understand how this can be translated into effective strategies for behaviour change.

If you are interested in exploring how behavioural science could apply to your goals and challenges, get in touch with one of our team for a chat.

Categories: Best Practice