AI in Education
Opening Survey
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Please complete this survey before exploring our OER. You will be asked similar questions at the end of our OER in hope to reflect on your learning during this experience.
How to Access the Survey
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> On a smartphone: click here to open in a separate tab!
Opening survey (laptop or tablet only)
The AI Phenomenon
Artificial Intelligence is a booming technological domain capable of altering every aspect of our social interactions."
- UNESCO, 2019a, p. 4
Artificial Intelligence (AI) is arguably the driving technological force of the first half of this century, and will transform virtually every industry, if not human endeavors at large."
- Holmes et al., 2019, p. 1
everyday ai
We've all heard of 'artificial intelligence' (AI). Indeed, many of us already use AI-based tools. Consider this list of common apps and devices:
The list goes on and on...
At the same time, there are many high claims about artificial intelligence becoming increasingly powerful and 'transforming' many areas of our lives; from the service industry, shipping/logistics and healthcare, to shopping, home maintenance and law enforcement.
Education has not been exempt. There are thousands of companies around the world promoting AI-based apps and services, many that promise to do amazing things like identify gaps in students' knowledge, provide virtual tutors, automate the creation of effective personalized learning plans, automatically grade essays, measure student engagement, and much more.
At the same time, there are many high claims about artificial intelligence becoming increasingly powerful and 'transforming' many areas of our lives; from the service industry, shipping/logistics and healthcare, to shopping, home maintenance and law enforcement.
Education has not been exempt. There are thousands of companies around the world promoting AI-based apps and services, many that promise to do amazing things like identify gaps in students' knowledge, provide virtual tutors, automate the creation of effective personalized learning plans, automatically grade essays, measure student engagement, and much more.
ACTIVITY 1
Experience a chatbot in a way that gives some insight into how its AI algorithm works behind the scenes. (A chabtbot is a computer system designed to simulate conversation with human users, usuly through typed interaction.)
The purpose of this activity is to visualize how chatbots distinguish and breakdown specific questions or commands into "decision trees"when asked by humans.
How to complete this activity:
Experience a chatbot in a way that gives some insight into how its AI algorithm works behind the scenes. (A chabtbot is a computer system designed to simulate conversation with human users, usuly through typed interaction.)
The purpose of this activity is to visualize how chatbots distinguish and breakdown specific questions or commands into "decision trees"when asked by humans.
How to complete this activity:
- Click on the image below to start chatting (type chat) with Ivy. (A new tab will open featuring "Ivy the Chatbot". You may need to click on the chat bubble icon at bottom right!)
- You will play the role of a university student seeking answers to questions you have about the campus, your courses, financial assistance, etc. (Make up your own questions.)
- When Ivy answers you, it displays an interactive diagram representing some of "decision tree" structure of its algorithm
- Play with the elements of the diagram to get a sense of the decisions Ivy had to make to formulate its answer
- After your Ivy experience, add a post to the Padlet below, using your name as your post title when answering the guiding questions you see at the top of the Padlet.
Chatbots Save Money, Free Up Human Workers
The schools where Ivy is deployed are using it in a very targeted way. It's not replacing human information providers, merely supplementing them; freeing them to handle the kinds of interactions that are too complex for AI to handle, while the chatbot addresses simple, fact-based queries.
While the amount of money saved will vary from case to case, some schools that use chatbots have indicated it provides much quicker response times than their human-staffed call center can, for a large class of simple queries that are well suited to chatbots like Ivy (see this case study video: https://vimeo.com/322805216 ).
The schools where Ivy is deployed are using it in a very targeted way. It's not replacing human information providers, merely supplementing them; freeing them to handle the kinds of interactions that are too complex for AI to handle, while the chatbot addresses simple, fact-based queries.
While the amount of money saved will vary from case to case, some schools that use chatbots have indicated it provides much quicker response times than their human-staffed call center can, for a large class of simple queries that are well suited to chatbots like Ivy (see this case study video: https://vimeo.com/322805216 ).
The AI for Education Market
Intro
Trained algorithms are already better at recognizing breast cancer than expert radiologists. I welcome AI into the education field. If we don't have processed, filtered, relevant information with which to make decisions, we're just attempting to optimize education through trial and error."
- Chris Spanis, OER Team Member
INTRO
The following key international indicators of the importance of AI have drastically increased in the last 10 years:
- Investment in educational AI start-ups
- Growth in academic research on AI
- AI conference attendance
- Enrollment in AI degree programs
- Government and non-profit initiatives to nurture AI expertise, products and services
- Attendance at AI conferences continues to increase significantly. In 2019, the largest, NeurIPS, expects 13,500 attendees, up 41% over 2018 and over 800% relative to 2012. Even conferences such as AAAI and CVPR are seeing annual attendance growth around 30%.
- Between 1998 and 2018, the volume of peer-reviewed AI papers has grown by more than 300%, accounting for 3% of peer-reviewed journal publications and 9% of published conference papers.
- In the US and Canada, the number of international PhD students graduating in AI continues to grow, and currently exceeds 60% of the PhDs produced from these programs (up from less than 40% in 2010).
Trusted non-profit organizations like the publicly and privately funded Vector Institute have been established to promote AI-based innovation across all industry sectors.
Presented in this video, The international Society for Technology in Education encourages educators to learn how to teach AI concepts to students because AI is already significantly impacting society.
Below is a sneak peak of how a school in ShangHai, China is putting AI to use. What are your thoughts?
In 2019, IDC reported that global spending on AI increased by 44% over 2018 and was expected to reach $35.98B by the end of 2019 and $79.2 B in 2022. After government and consumer services, education was predicted to experience the third largest growth of any sector, with 44% growth from 2018 to 2022 (Nagel, 2019).
The global AI in education market size was estimated by Global Market Insights at over USD 400 million in 2017 and is anticipated to grow at a compound annual growth rate (CAGR) of more than 45% from 2018 to 2024:
Because of all these developments, education leaders and decision makers are feeling pressure to figure out how to leverage AI technologies for their institutions, districts and schools.
This presents an opportunity for education system innovators and investors to engage with leading-edge AI improvements that bring real value.
Hype
In three hours we understand students more than the three years spent by the best teachers”
- Derek Li, Founder of Squirrel AI
Cognii’s exclusive technology can automatically assess and give feedback on short written answers to open-response questions, across content areas and difficulty levels. Cognii [also] offers adaptive, personalized learning experiences... Students get exactly the questions and coaching they need”
- Cognii website
THE HYPE
The quotes shown above are bold and provocative, and fairly typical of claims made by various educational AI companies. The capabilities of such AI tools for learning sound truly amazing, but do they actually work?
Hype surrounding new educational technology (EdTech) most often starts with the EdTech companies who are trying to promote their products and services.
These promotions are often inspired by previous media coverage of the success that the underlying technology has had in other domains. Examples include: autonomous parking assist, self driving vehicles, Amazon Alexa etc.
The language contained in these promotions is often picked up by other EdTech enthusiasts, bloggers and online commentators who then repeat these promotional claims, without actually referencing any evidence; they simply just cite the company’s website.
There are some articles that combine both the manufacturer of the technology’s claims, as well as other primary sources about related technologies. The contrast in these articles make the EdTech companies' claims appear to be more plausible, as readers are confused between what is actually fact, and what is part of the companies' promotional strategies.
These promotions are often inspired by previous media coverage of the success that the underlying technology has had in other domains. Examples include: autonomous parking assist, self driving vehicles, Amazon Alexa etc.
The language contained in these promotions is often picked up by other EdTech enthusiasts, bloggers and online commentators who then repeat these promotional claims, without actually referencing any evidence; they simply just cite the company’s website.
There are some articles that combine both the manufacturer of the technology’s claims, as well as other primary sources about related technologies. The contrast in these articles make the EdTech companies' claims appear to be more plausible, as readers are confused between what is actually fact, and what is part of the companies' promotional strategies.
This article from the Edvocate website cites no original research and refers to two other articles...
...and those two articles in turn refer to the same three EdTech companies that manufacture the products under review. (The TNW article is one that cites both EdTech companies and academic researchers.)
These are the three companies referenced by the two articles:
Despite the rapid spreading of these promotions, the actual products and services mentioned may not satisfy their consumers expectations. Educators and investors should carefully modify their expectations of the products in this market.
Reality
The areas with the biggest potential for automation are preparation, administration, evaluation, and feedback...actual instruction, engagement, coaching, and advising are more immune to automation.”
- How artificial intelligence will impact K-12 teachers (McKinsey & Company report), 2020
Although various AI advocates are currently touting a myriad
of new applications for K–12 education, there is little evidence yet to support the usefulness of these applications to districts, schools, and teachers.”
- Robert F. Murphy, PhD., Carnegie Mellon University, 2019
reality
The reality of AI as of early this year is summarized by the quotes above. There are many benefits promised by AI-based EdTech that are also not proven. The most realistic applications of AI are those that include lower level analyses that are data-driven, rather than those that include human-like cognitive capacities.
The starting point for a lot of educational AI ventures is academic research, however the references that sales materials cite do not necessarily support the features that they claim to be capable of.
Definitive numbers on how many schools are currently using AI systems are not readily available, but we know that tools like "early warning systems" in higher ed. (e.g. for dropout rate and pass rate tracking) have been the most common AI tool used in education thus far. Another tool very frequently used in K-20 are the rules-based (simpler) adaptive learning systems like Rosetta Stone, ALEKS, etc.. There are some survey data on WHY AI is being adopted and WHAT TYPE of AI applications educational institutions are using available, and it's apparent that some products are quite well established. IXL, for example, is used by more than 42,000 schools in the USA, serving "95 of the top 100 districts".
Inherent, significant risk factors have consistently been identified by academic researchers and responsible AI companies. These factors can be summarized as:
Ethics in AI is a critical area of study that must be seriously engaged by thought leaders in all sectors. The three articles linked below give a good idea of the complexity of ethical issues involved with developing and implementing AI and machine learning in education and other areas of society.
The starting point for a lot of educational AI ventures is academic research, however the references that sales materials cite do not necessarily support the features that they claim to be capable of.
Definitive numbers on how many schools are currently using AI systems are not readily available, but we know that tools like "early warning systems" in higher ed. (e.g. for dropout rate and pass rate tracking) have been the most common AI tool used in education thus far. Another tool very frequently used in K-20 are the rules-based (simpler) adaptive learning systems like Rosetta Stone, ALEKS, etc.. There are some survey data on WHY AI is being adopted and WHAT TYPE of AI applications educational institutions are using available, and it's apparent that some products are quite well established. IXL, for example, is used by more than 42,000 schools in the USA, serving "95 of the top 100 districts".
Inherent, significant risk factors have consistently been identified by academic researchers and responsible AI companies. These factors can be summarized as:
- algorithmic bias (algorithms that reflect limited perspectives or biases of their creators)
- low data set quality (the failure of data sets that feed machine learning to accurately describe the richness and diversity of the domain they purport to represent)
- poor stewardship of personal data (failure to follow legislated and ethical collection, use and dissemination of personal information)
- lack of AI model transparency (over-protection of a company's AI architecture resulting in a lack of public trust in their product or service).
Ethics in AI is a critical area of study that must be seriously engaged by thought leaders in all sectors. The three articles linked below give a good idea of the complexity of ethical issues involved with developing and implementing AI and machine learning in education and other areas of society.
The security measures we’ve typically seen to safeguard the data collected through adaptive learning systems are:
- strong encryption for transmitted and stored data
- policies to never share or sell data to third parties or use data to build marketing profiles
- promises to abide by legislated restrictions in the clients' or the vendor's respective jurisdictions (e.g. GDPR in the EU, COPPA in the USA, PIPEDA in Canada, etc.).
Making matters worse, key terms like "adaptive learning", "machine learning" and even "artificial intelligence" itself are only vaguely understood by many people. Some companies take advantage of the lack of widely accepted definitions and promote their products as advanced AI technologies when in fact they are only using conditional logic algorithms or rudimentary AI as opposed to the more sophisticated AI that the more sensational predictions of future capabilities rely on.
The next section clarifies AI concepts and terms, which prepares us to hear critical advice from recognized experts and then to understand the most feasible AI applications for education.
Key Definitions & Concepts
Understanding the following concepts and terminology will greatly demystify key aspects of artificial intelligence. This paves the way to making informed decisions about how to best invest in educational AI.
While there are no single, universal definitions for these basic terms, the following are working definitions for education system innovators that we've adopted from a collection of authoritative historical and contemporary sources compiled by UNESCO (UNESCO, 2019a) and from an article by Carnegie Mellon professor Robert F. Murphy.
Open the box below to read our WORKING DEFINITIONS...
While there are no single, universal definitions for these basic terms, the following are working definitions for education system innovators that we've adopted from a collection of authoritative historical and contemporary sources compiled by UNESCO (UNESCO, 2019a) and from an article by Carnegie Mellon professor Robert F. Murphy.
Open the box below to read our WORKING DEFINITIONS...
working DEFINITIONS
Artificial Intelligence (AI) -"Software algorithms and techniques that allow computers and machines to simulate human perception and decision making processes to successfully complete tasks" (Murphy, 2019, p. 2).
Narrow AI / Weak AI - Software that performs a single, specific function (e.g. Google Assistant responding to a question about today's weather; a driverless car recognizing a stop sign as different from a yield sign)
Strong AI / Artificial General Intelligence (AGI) - Theoretical (non-existent) software that approaches the cognitive reasoning abilities of humans.
Machine Learning - The function of a computer system designed to gradually be able to perform important tasks by using algorithms to generalize from provided examples; automatically improving with experience.
When a machine "learns" it is using statistical algorithms to create a prediction model by processing large amounts of rich, varied data about the subject its learning.
Deep Learning - A more complex form of machine learning that uses layers of algorithms to be able to generalize without being provided with examples.
Big Data - The practice of working with collections of data that are so large and complex that traditional data management methods are ineffective, requiring newer, more sophisticated data management technologies.
There are "four Vs" that typically need to be considered with big data:
Volume: how much data are there?
Variety: how diverse are the data?
Velocity: how quickly are new data generated?
Veracity: how accurate are the data?
Data Analytics (DA) - The practice of using computer systems to examine raw data so that meaning can be made from them.
Learning Analytics - Data analytics applied to teaching and learning to identify learner habits, predict learner responses, provide timely feedback, support decision-making, simplify realistic assessments and provide personal supervision of learners’ progress.
Adaptive Learning - A computer-based educational system that changes the type of content, the rate of progression through increasingly sophisticated types of content and other content elements in response to student performance on the system.
Examples of established adaptive learning systems include ALEKS, Dreambox Learning, and MATHia.
Narrow AI / Weak AI - Software that performs a single, specific function (e.g. Google Assistant responding to a question about today's weather; a driverless car recognizing a stop sign as different from a yield sign)
Strong AI / Artificial General Intelligence (AGI) - Theoretical (non-existent) software that approaches the cognitive reasoning abilities of humans.
Machine Learning - The function of a computer system designed to gradually be able to perform important tasks by using algorithms to generalize from provided examples; automatically improving with experience.
When a machine "learns" it is using statistical algorithms to create a prediction model by processing large amounts of rich, varied data about the subject its learning.
Deep Learning - A more complex form of machine learning that uses layers of algorithms to be able to generalize without being provided with examples.
Big Data - The practice of working with collections of data that are so large and complex that traditional data management methods are ineffective, requiring newer, more sophisticated data management technologies.
There are "four Vs" that typically need to be considered with big data:
Volume: how much data are there?
Variety: how diverse are the data?
Velocity: how quickly are new data generated?
Veracity: how accurate are the data?
Data Analytics (DA) - The practice of using computer systems to examine raw data so that meaning can be made from them.
Learning Analytics - Data analytics applied to teaching and learning to identify learner habits, predict learner responses, provide timely feedback, support decision-making, simplify realistic assessments and provide personal supervision of learners’ progress.
Adaptive Learning - A computer-based educational system that changes the type of content, the rate of progression through increasingly sophisticated types of content and other content elements in response to student performance on the system.
Examples of established adaptive learning systems include ALEKS, Dreambox Learning, and MATHia.
Apart from the understanding the key terms given in the expandable box above, it's important to know that AI in education comes in two broad categories:
AI Category |
Features |
Examples |
Rule-Based / Knowledge-Driven Systems |
Make decisions based on expert processing rules programmed by humans |
Simpler adaptive learning systems, intelligent tutoring systems |
Machine Learning / Data-Driven Systems |
Make predictions using auto-generated rules and based on analysis of large, rich data sets |
More complex adaptive learning systems, automated essay scoring, early warning systems for poor academic performance and late graduation or dropout |
Activity #2
Please complete this activity before continuing to the next page.
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Please complete this activity before continuing to the next page.
How to Access this Activity
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ACTIVITY #2 (laptop or tablet only)