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Common AI Techniques in L&D

by | May 8, 2021

Below is a broad category of commonly used AI tools and techniques being applied in the L&D space. There are some overlaps of functions and features between categories. For examples, some chatbots also offer an adaptive learning experience by suggesting personalized next steps within the chatbot responses; adaptive learning platforms typically analyze learners’ responses, recommend learning paths and content as well as offer some kind of learning analytics and to some extent learning predictions.

 

Chatbots / Conversational Agents

Chatbots are software applications that mimic written or spoken human speech for the purposes of simulating a conversation with a real person. It is commonly used for customer support and general communication with customers in businesses and can also be used in learning applications.

 

Pros

Cons

  • Can provide quick answers to commonly asked questions
  • Simple to use (i.e. simple user interface that people are already familiar with) and always available
  • Can pull information from a variety of sources (e.g. Dropbox, Google Drive, intranet, etc.) and deliver that to learners within the chatbot message box
  • Ability to provide personalized/adaptive feedback to learners
  • Low bandwidth required for text-based chatbots (or no bandwidth if chatbot messages are delivered via SMS)
  • Potential to use voice interface for voicebots
  • Natural Language Processing (NLP) still has some gaps
  • Many bots on the market but quality varies
  • Bias in content selection or incomplete content selection
  • Context is still missing, thus difficult to have deeper or more meaningful conversations
  • Potentially causes mistrust of the system and the learning content when chatbots push out wrong information/not able to help (due to lack of context or gaps in content selection)
  • There is a lack of research in evaluating the effectiveness of chatbots used in learning especially in terms of supporting self-regulation and self-efficacy in learners

Adaptive Learning Experience Platforms

Previously known as Intelligent Tutoring Systems (ITS), they have evolved into adaptive learning platforms with pathways that updates dynamically as learners gain competencies/knowledge. Some adaptive learning experience platforms also monitor learners’ progress (their successes, misconceptions, misses) and provide personalized step-by-step instructions.

 

Pros

Cons

  • Good for content curation
  • Recommender system – source a variety of content based on learner profiles, knowledge levels, and preferences
  • Can adapt content and activities to the right level of knowledge/skill
  • If it is well-designed, it can create a learner-centred experience that can be sensitive to the learner’s needs as well as context
  • Potential application of sound pedagogical principles to structure learning that will meet the learning objectives and competencies
  • Can possibly provide personalized experience in large online courses (i.e. MOOC)
  • Could be too prescriptive
  • These platforms are designed by tech people – they decide on which data is relevant in order to categorize learners in certain ways, which are often indicators that don’t necessarily help you arrive at a conclusion or to provide learning interventions
  • If the adaptive pathways are being pre-defined by someone (either tech people or instructional designers or both) – what assumptions and bias do they have? There aren’t a lot of good practices and guidance on this front
  • Context is still missing, thus difficult to have deeper or more meaningful conversations
  • You don’t know what you don’t know – how do you know what you missed out on if you go down a particular adaptive pathway
  • Difficult to evaluate as most algorithms are opaque and not open for review (or too complex for educators to review)

 

Content Creation and Management

AI-enabled content creation allows content to be generated at scale.

 

Pros

Cons

  • Save time and effort on routine-based tasks
  • Auto-tagging, indexing, and metadata
  • Potential to scale content
  • Localization and globalization functions
  • AI technologies such as Deep Fakes have a bad reputation and promote a lack of trust
  • There is a lack of quality control/standards in place
  • If the adaptive pathways are being pre-defined by someone (either tech people or instructional designers or both) – what assumptions and bias do they have? There aren’t a lot of good practices and guidance on this front
  • Tweaking and preparing the information upfront still requires fairly labour-intensive work

Predictive Analytics / Learning Analytics

A relatively new yet growing field, learning analytics is the measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs for the purpose of understanding and optimizing learning and its impact on an organization’s performance.

 

Pros

Cons

  • Can be used to understand learners, the learning experience, and learning programs overall
  • Ability to turn data into insights and actions for instructional designers and instructors to use to improve learning
  • Can be used to augment human decision-making
  • Potential use of formative assessment data
  • Too much data can be a bad thing especially when data collected are not meaningful or actionable
  • Surveillance and control issue – who own the data? Can learners choose to opt out?
  • Mistrust of the system when predictions are inaccurate
  • Inaccurate predictions could potentially demotivate learners/impact their confidence

Auto Grading and Assessments

Automatic grading of written essays with the ability for the robo-graders to sort and “learn” what good essays look like and be able to assign grades accordingly. Some of the systems claim to have the ability to improve over time based on the massive amount of data input.

 

Pros

Cons

  • Save time and reduce cost
  • Can potentially complement human instructors in assessing quality of content
  • Can scale up for large learner groups
  • Technologies have improved significantly since the early days
  • The whole is greater than the sum of its parts – easy to miss the whole picture by focusing on the specifics
  • Pattern matching could be misleading
  • Still missing nuances/context
  • Can easily trick the system and there are many skepticism around this*

Augmented and Virtual Reality

Augmented Reality (AR) and Virtual Reality (VR) are two technologies that are maturing rapidly for training and learning purpose. AR overlays digital elements to a real-world view often by using the camera on a smartphone while VR provides a complete immersion experience that shuts out the physical world. AR and VR both make use of AI techniques such as natural language processing, image recognition, and selective rendering.

 

Pros

Cons

  • Makes simulations more realistic (i.e. More likely to enable transfer of learning from simulated environment to actual environment)
  • Enables experiential learning
  • Able to support creative, non-linear learning*
  • Hardware is still a limitation
  • Expensive to produce, update, and maintain
  • Slow or inconsistent framerate impacts performance
  • VR interface makes some tasks more difficult to do
  • User privacy and data controls on data tracking features have not been addressed adequately