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Artificial Intelligence in Teaching (AIT): A road map for future developments

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Abstract

The AIT project started in 2019 and aims to identify and analyse AI best practices in HE in three countries to develop a road map for future developments and use of AI. The AIT project will investigate three dimensions of AI in education: learning ‘for’ AI, learning ‘about’ AI, learning ‘with’ AI. The focus will be on identifying examples and best practices of AI in HE (across all AI dimensions). The analyses will include outlining national characteristics, specific technologies, and didactic and pedagogical approaches to AI in HE in the United Kingdom, Portugal and Denmark.
Artificial Intelligence in Teaching (AIT):
A road map for future developments
José Bidarra, Universidade Aberta, Portugal,
Henrik Køhler Simonsen, SmartLearning, Denmark,
Wayne Holmes, Nesta, United Kingdom
Project 2019-1-DK01-KA203-060293 W
1. The promise of AI in Higher Education
2. The stakeholders of AI in Higher Education
3. The context of Erasmus+ Project AIT
4. Project objectives and expected outcomes
5. National cases and strategies
6. Dissemination
Project 2019-1-DK01-KA203-060293 W
The promise of AI in Higher Education (HE)
-Artificial Intelligence (AI), is defined as “the use of computer systems designed to
interact with the world through capabilities and behaviours that we think of as
essentially human”. (Luckin et al., 2016)
Project 2019-1-DK01-KA203-060293 W
The promise of AI in Higher Education (HE)
-Artificial Intelligence (AI), is defined as “the use of computer systems designed to
interact with the world through capabilities and behaviours that we think of as
essentially human”. (Luckin et al., 2016)
-AI is already a part of life. For instance, personal agents, such as Siri, Alexa or
Cortana, and algorithms that bring us personalised recommendations, for instance in
Amazon or Netflix.
Project 2019-1-DK01-KA203-060293 W
The promise of AI in Higher Education (HE)
-Artificial Intelligence (AI), is defined as “the use of computer systems designed to
interact with the world through capabilities and behaviours that we think of as
essentially human”. (Luckin et al., 2016)
-AI is already a part of life. For instance, personal agents, such as Siri, Alexa or
Cortana, and algorithms that bring us personalised recommendations, for instance in
Amazon or Netflix.
-AI-powered learning systems are increasingly being deployed in schools, colleges
and universities.
Project 2019-1-DK01-KA203-060293 W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Learning
about AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Learning
about AI
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Learning
about AI
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
Learning
about AI
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Teaching
teachers
about AI
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Teaching
teachers
about AI
Training
tomorrow’s
AI engineers
Learning for
AI
W
The promise of AI in Higher Education (HE)
Project 2019-1-DK01-KA203-060293
Artificial
Intelligence
in education
Learning with
AI
Student-
facing AI
Teacher-
facing AI
System-
facing AI
Learning
about AI
Teaching
young
people
about AI
Teaching
teachers
about AI
Training
tomorrow’s
AI engineers
Learning for
AI
Learning to
live with AI
W
Some questions for you:
-What examples of learning with AI in higher education do you know?
-What examples of learning about AI in higher education do you know?
-What examples of learning for AI in higher education do you know?
Project 2019-1-DK01-KA203-060293 W
The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
W
The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
W
The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
Teachers
W
The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
Teachers
Researchers
W
The stakeholders of AI in HE
Project 2019-1-DK01-KA203-060293
AI in
HE
Students
Teachers
Researchers
Decision
makers
W
The context of the AIT project
-Artificial Intelligence (AI) is fast becoming ubiquitous. In particular, it is having a critical
but unclear impact on Higher Education (HE) practices, educators and learners
across Europe, which urgently needs to be properly understood.
Project 2019-1-DK01-KA203-060293 H
The context of the AIT project
-Artificial Intelligence (AI) is fast becoming ubiquitous. In particular, it is having a critical
but unclear impact on Higher Education (HE) practices, educators and learners
across Europe, which urgently needs to be properly understood.
-The “Artificial Intelligence in Teaching” Erasmus+ project (AIT) aims to identify and
analyse AI best practices in HE in three countries and to develop a roadmap for
future developments and use.
Project 2019-1-DK01-KA203-060293 H
The context of the AIT project
-Artificial Intelligence (AI) is fast becoming ubiquitous. In particular, it is having a critical
but unclear impact on Higher Education (HE) practices, educators and learners
across Europe, which urgently needs to be properly understood.
-The “Artificial Intelligence in Teaching” Erasmus+ project (AIT) aims to identify and
analyse AI best practices in HE in three countries and to develop a roadmap for
future developments and use.
-The AIT project seeks to uncover national characteristics, specific technologies, and
didactic and pedagogical approaches to AI in HE in the United Kingdom, Portugal
and Denmark.
Project 2019-1-DK01-KA203-060293 H
AIT project objectives
A. To identify and analyse practical examples of AI in HE.
B. To identify and analyse best practices of AI in HE.
C. To identify national approaches to AI in HE.
D. To develop a roadmap for the development of AI in HE.
E. To disseminate knowledge about AI in HE.
These objectives are all related to the national characteristics of the countries in
the project: Denmark, Portugal and the United Kingdom.
Project 2019-1-DK01-KA203-060293 H
AIT project expected outcomes
i. increased knowledge of the dimensions of AI at all levels in the HE
sector,
ii. research-based data of AI technologies in use across the HE sector,
iii. a research-based roadmap for future development and use of AI in HE,
iv. increased knowledge of AI for informing institutional decisions and
policymaking across Europe.
Project 2019-1-DK01-KA203-060293 H
Study of national cases (DK, UK, PT)
-Identification of cases and activities;
Project 2019-1-DK01-KA203-060293 H
Study of national cases (DK, UK, PT)
-Identification of cases and activities;
-Classification of focus area (some categories):
Intelligent Tutoring Systems;
Dialogue-based Tutoring Systems;
Exploratory Learning Environments;
Automatic writing evaluation;
Language Learning;
Tutoring chatbots;
Learning analytics;
Augmented and Virtual Reality.
Project 2019-1-DK01-KA203-060293 H
National characteristics (DK, UK, PT)
-Overview of EU principles on AI in HE
-National digital maturity and strategies for AI
-Analysis of national AI focus areas
-Overview of HE system and national strategy for AI in HE
-Evidence of AI in HE (research, practice / with, for, about AI)
Project 2019-1-DK01-KA203-060293 H
AIT project preliminary outcomes
-AI is widely taught and researched in HE (i.e., learning about AI).
Project 2019-1-DK01-KA203-060293 J
AIT project preliminary outcomes
-AI is widely taught and researched in HE (i.e., learning about AI).
-The impact of AI on human lives (i.e., learning for AI) is not widely
taught in HE.
Project 2019-1-DK01-KA203-060293 J
AIT project preliminary outcomes
-AI is widely taught and researched in HE (i.e., learning about AI).
-The impact of AI on human lives (i.e., learning for AI) is not widely
taught in HE.
-AI is not widely used to support learning in HE (i.e., learning with AI).
Project 2019-1-DK01-KA203-060293 J
Emerging framework
Project 2019-1-DK01-KA203-060293
Learning
with AI
Learning
about AI
Learning
for AI
J
Emerging framework
Project 2019-1-DK01-KA203-060293
Learning
with AI
Learning
about AI
Learning
for AI
Intelligent
Tutoring
Systems
Dialogue-
based
Tutoring
Systems
Exploratory
Learning
Environments
Automatic
writing
evaluation
ITS+ Language
Learning Chatbots
Augmented
and Virtual
Reality
Learning
Network
Orchestrators
Learning
Analytics
( )
J
National examples of learning with AI in HE
PT
-ABC Teach (learning
analytics, fuzzy logic and
affective computing)
-Learning Scorecard
(descriptive learning
analytics)
-ModEst (temporal data
mining, predictive analytics,
Markov chain modelling)
Project 2019-1-DK01-KA203-060293 J
National examples of learning with AI in HE
PT
-ABC Teach (learning
analytics, fuzzy logic and
affective computing)
-Learning Scorecard
(descriptive learning
analytics)
-ModEst (temporal data
mining, predictive analytics,
Markov chain modelling)
Project 2019-1-DK01-KA203-060293
DK
-Area 9
(an intelligent tutoring
system)
-Damvad Analytics
(learning analytics, Southern
Denmark University)
-AI in Business Economics
(exploratory learning
environment
J
National examples of learning with AI in HE
PT
-ABC Teach (learning
analytics, fuzzy logic and
affective computing)
-Learning Scorecard
(descriptive learning
analytics)
-ModEst (temporal data
mining, predictive analytics,
Markov chain modelling)
Project 2019-1-DK01-KA203-060293
UK
-Ada
(a student-support chatbot,
Bolton College)
-OU Analyse
(learning analytics, Open
University)
-Scholarly Knowledge Mining
(KMI)
DK
-Area 9
(an intelligent tutoring
system)
-Damvad Analytics
(learning analytics, Southern
Denmark University)
-AI in Business Economics
(exploratory learning
environment
J
Dissemination
-Erasmus+ Project Results Platform;
-Project website https://learninghub.smartlearning.dk/projekter/artificial;
-Social networking (Facebook, LinkedIn, Twitter, Instagram);
-Websites, newsletters and press releases by each institution;
-Workshops, seminars and a final conference;
-Articles published in Journals and conferences.
Project 2019-1-DK01-KA203-060293 J
Some answers from you:
Project 2019-1-DK01-KA203-060293 J
?
References
-Holmes, W., Bialik, M. and Fadel, C. (2019). Artificial Intelligence in Education: Promises and
Implications for Teaching and Learning. Boston, MA: The Center for Curriculum Redesign.
-Kukulska-Hulme, A., Beirne, E., Conole, G., Costello, E., Coughlan, T., Ferguson, R., FitzGerald, E.,
Gaved, M., Herodotou, C., Holmes, W., Mac Lochlainn, C., Nic Giollamhichil, M., Rienties, B.,
Sargent, J., Scanlon, E., Sharples, M. and Whitelock, D. (2020). Innovating Pedagogy 2020:
Open University Innovation Report 8. Milton Keynes: The Open University.
-Luckin, R., Holmes, W., Forcier, L. and Griffiths, M. (2016). Intelligence Unleashed. An Argument
for AI in Education. London: Pearson.
-Zawacki-Richter, O., Marín, V. I., Bond, M. and Gouverneur, F. (2019) ‘Systematic review of
research on artificial intelligence applications in higher education where are the
educators?’, International Journal of Educational Technology in Higher Education, vol. 16, no.
1 [Online]. DOI: 10.1186/s41239-019-0171-0
Project 2019-1-DK01-KA203-060293 J
... That is the conceptual vision of this paper. However, it needs some solid theoretical building blocks and, for that purpose, the framework presented in Holmes et al. (2019), and later refined and expanded in Bidarra et al. (2020) should be presented. ...
... The next theoretical building block is the emerging framework outlining existing AIED technologies based on Holmes et al. (2019). This approach has been updated and streamlined by Bidarra et al. (2020) and is shown below in These two theoretical building blocks make it possible to categorize the overall purpose of an AI application and to present an overall description of the AI solution in question. ...
... Having selected the learning type, then it is time to select the AI learning types, cf. Holmes et al. (2019) and Bidarra et al. (2020). The educator now selects the AI learning type in question, that is, whether it is Learning with AI, Learning about AI or Learning for AI by turning the second light-grey wheel. ...
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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.
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This paper on artificial intelligence in education (AIEd) has two aims. The first: to explain to a non-specialist, interested, reader what AIEd is: its goals, how it is built, and how it works. The second: to set out the argument for what AIEd can offer teaching and learning, both now and in the future, with an eye towards improving learning and life outcomes for all. Computer systems that are artificially intelligent interact with the world using capabilities (such as speech recognition) and intelligent behaviours (such as using available information to take the most sensible actions toward a stated goal) that we would think of as essentially human. At the heart of artificial intelligence in education is the scientific goal to make knowledge, which is often left implicit, computationally precise and explicit. In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also designed to be a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us more fine-grained understandings of how learning actually happens. Although some might find the concept of AIEd alienating, the algorithms and models that underpin ed tech powered by AIEd form the basis of an essentially human endeavor. Using AIEd, teachers will be able to offer learners educational experiences that are more personalised, flexible, inclusive and engaging. Crucially, we do not see a future in which AIEd replaces teachers. What we do see is a future in which the extraordinary expertise of teachers is better leveraged and augmented through the thoughtful deployment of well designed AIEd. We have available, right now, AIEd tools that could support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles. In the near future, we expect that teaching and learning will increasingly be supported by the thoughtful application of AIEd tools. For example, by lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies - in and beyond school - and new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. If we are ultimately successful, we predict that AIEd will help us address some of the most intractable problems in education, including achievement gaps and teacher retention. AIEd will also help us respond to the most significant social challenge that AI has already brought - the steady replacement of jobs and occupations with clever algorithms and robots. It is our view that this provides a new innovation imperative in education, which can be expressed simply: as humans live and work alongside increasingly smart machines, our education systems will need to achieve at levels that none have managed to date. True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will also enable system-level data collation and analysis that will help us to learn much more about learning itself – and how to improve it. Moving forward, we will need to pay close attention to three powerful forces as we map the future of artificial intelligence in education, namely pedagogy, technology, and system change. Paying attention to the pedagogy will mean that the design of new edtech should always start with what we know about learning. It also means that the system for funding this work must be simultaneously opened up and refocused, moving away from isolated pockets of R&D and toward collaborative enterprises that prioritise areas known to make a real difference to teaching and learning. Paying attention to the technology will mean creating smarter demand for commercial grade AIEd products that work. It also means the development of a robust, component-based AIEd infrastructure, similar to the smartphone app marketplace, where researchers and developers can access standardised components that have been developed in collaboration with educators. Paying attention to system change will mean involving teachers, students, and parents in co-designing new tools, so that AIEd will appropriately address the inherent “messiness” of real classroom, university, and workplace learning environments. It also means the development of data standards that promote the safe and ethical use of data. Said succinctly, we need intelligent technologies that embody what we know about great teaching and learning, embodied in enticing consumer grade products, which are then used effectively in real-life settings that combine the best of human and machine. We do not underestimate the new-thinking, inevitable wrong-turns, and effort required to realise these recommendations. However, if we are to properly unleash the intelligence of AIEd, we must do things differently - via new collaborations, sensible funding, and (always) a keen eye on the pedagogy. The potential prize is too great to act otherwise.
Artificial Intelligence in Education: Promises and Implications for Teaching and Learning
  • W References -Holmes
  • M Bialik
  • C Fadel
  • A Kukulska-Hulme
  • E Beirne
  • G Conole
  • E Costello
  • T Coughlan
  • R Ferguson
  • E Fitzgerald
  • M Gaved
  • C Herodotou
  • W Holmes
  • C Mac Lochlainn
  • M Nic Giollamhichil
  • B Rienties
  • J Sargent
  • E Scanlon
  • M Sharples
  • D Whitelock
-Articles published in Journals and conferences. References -Holmes, W., Bialik, M. and Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: The Center for Curriculum Redesign. -Kukulska-Hulme, A., Beirne, E., Conole, G., Costello, E., Coughlan, T., Ferguson, R., FitzGerald, E., Gaved, M., Herodotou, C., Holmes, W., Mac Lochlainn, C., Nic Giollamhichil, M., Rienties, B., Sargent, J., Scanlon, E., Sharples, M. and Whitelock, D. (2020). Innovating Pedagogy 2020: Open University Innovation Report 8. Milton Keynes: The Open University.