An overall view of the role of artificial intelligence in education. The role of faculty is to understand technology and its use in delivering meaningful and authentic personal learning experiences for the learners.

1. Artificial Intelligence
in Health Professional Education
AIHPE
Vaikunthan Rajaratnam

2. Agenda
• Defining the technology
• Digital education
• Digital health
• Types of AIHPE
• Personal experience

4. Defining the technology
• AI - leverages computers and machines to mimic
the problem-solving and decision-making
capabilities of the human mind
• Machine Learning - leverage labeled datasets -
- supervised learning, to inform its algorithm.
Require human intervention to process data
• Deep Learning - neural network comprised of
more than three layers—which would be inclusive
of the inputs and the output—can be considered a
deep learning algorithm.

5. Artificial Intelligence Algorithms
• Artificial Intelligence Algorithms
• Naive Bayes
• Decision Tree
• Random Forest
• Support Vector Machines
• K Nearest Neighbours
• Regression Algorithms
• Linear regression
• Lasso Regression
• Multivariate Regression
• Multiple Regression Algorithm
• Clustering Algorithms
• K-Means Clustering
• Fuzzy C-means Algorithm
• Expectation-Maximisation (EM) Algorithm
• Hierarchical Clustering Algorithm

6. Algorithms
• Basic Algorithm
• defined input leads to a defined output -
formulaic decision-making (Excel logic
function)
• Complex Algorithm
• defined output based off a set of
complex rules, calculations, or problem-
solving operations – formulaic+ complex
decision-making
• Artificial Intelligence
• outputs are not defined, but designated
• based on complex mapping of user data
• multiplied with each output
• decision based on collected information.
• system can improve its output based on
additional inputs

9. Input Data
Natural Language Processing
the automatic manipulation of natural language, like speech and text, by software.
Challenges
• Train machine reader
• With little labeled data
• Understand complex semantics
• Reason beyond explicitly stated in text

10. AI Learning with Indirect Supervision
• Unsupervised learning - algorithms to identify patterns in data
sets containing data points that are neither classified nor
labeled
• Statistical relational learning concerned with domain
models that exhibit both uncertainty (which can be dealt
with using statistical methods) and
complex, relational structure.
• Distant supervision - labeling data for relation extraction
utilizing an existing knowledge database.
• Incidental learning - learning that is unplanned or
unintended.
• Situated learning - attempts to build embodied intelligences
situated in the real world
• Grounded language learning - learning the meaning of natural
language units (e.g., utterances, phrases, or words)

11. Digital Education
• Artificial Intelligence in Education AIED
• Computer-Supported Collaborative Learning
CSCL
• Educational Data Mining EDM
• Learning Analytics LA

12. Big Data – 5V’s
Volume
• Size
• Amount
Velocity
• Capture
• Storage
• Management
Variety
• Diversity
• Range of
types
Veracity
• Accuracy
of data
Value
• Insight
discovery
and
• Pattern
recognition

13. Leitner, P., Khalil, M., & Ebner, M. (2017). Learning Analytics in Higher Education—A Literature Review. In A. Peña-Ayala (Ed.), Learning Analytics: Fundaments,
Applications, and Trends: A View of the Current State of the Art to Enhance e-Learning (pp. 1–23). Springer International Publishing. https://doi.org/10.1007/978-3-
319-52977-6_1

14. Learning Analytics Specification – MoodleDocs
(API - application programming interface)
NOUN
VERB
ADJECTIVE

15. Role of Big Data in Education
Learners
• Identifying predicting learning status
• recommending learning resources and activities
• sharing and improving the learning experience
Teachers
•receive feedback
•examining both the learning and the behaviour of the learners
•identifying the students who need support
•determining which mistakes occur more often and improving the effectiveness of some activities
Course
developers
• evaluate the courses’ structure and its impact on learning
• assessing course materials
• Identifying data mining based on different tasks and developing learning mode
Admin
• organize resources
• improving their offer of educational programs
• assessing both teachers and curricula effectiveness

16. AI in education
• Efficiency in medical teaching (Zhao et al., 2018).
• Meaningful learning experience
• Intelligent tutoring systems (ITS)
• Collaborative learning
• Motivation
• Better feedback
• Tutoring
• But does not provide a better environment for self-regulated learners
• Achieving learning outcome with trained Robot (Lin et al., 2018).
• Peer review process with computational support
• User Generated AI courses (Kandlhofer et al., 2016)

17. • AI in physical learning spaces
• Understand Intelligent Tutor Systems
• Adopt deep learning algorithm
• NLP for precision/personalised learning
• Cognitive neuroscience & Research on learning

18. Intelligent Adaptive Learning System
(IALS)
• AI system simulated human teacher
• Personalized learning plan
• One-on-one tutoring
• 5 to 10 times higher efficiency

19. Challenges
• Inaccurate or incomplete data
• Wrong technology adaptive learning systems - use machine learning
• Privacy concerns
• Information ownership
• Organizational readiness
• Ethics and accountability
• Socio economic – jobs/social isolation
• Future research – AI curriculum, personalised learning, faculty
development, Intelligent assessment
Tahiru, F. (2021). AI in Education: A Systematic Literature Review. Journal of Cases on Information Technology (JCIT), 23(1), 1–20.
https://doi.org/10.4018/JCIT.2021010101

20. Digital Health & AIHPE
AIHPE

21. Current Digital Learning
Learns material
presented
Diagnostic
assessment MCQ
Feedback
Remediation
threshold initiated
Progression

22. Proposed AIHPE
Personalised
material presented
Formal and informal
assessment for
learning NLP
Automated
personalised
feedback
Intelligent Tutor
System
Remediation
curriculum
Achievement
recognition
Peer Assessment

24. Four core elements of AIHPE
The learner
(Digital
Twin*?)
Faculty
(SME)
The Teacher Interfaces
*virtual representation of a physical item (avatar)

25. Types of AI in Education
Automation of Administrative Task
• Formative evaluation
• Automatic grading
Smart Content
• Dynamic
• Changes based behavior
• Relevant and personalized experience
Intelligent Tutoring System (ITS)
• Natural Language Processing (NLP)
• Intelligent algorithms
• Customized, immediate and automated instruction/feedback
• Student, tutor and expert.

26. AI in Medical
Education
• No functional change
Substitution
• With functional change
Augmentation
• Significant task changes
Modification
• Creating new and inconceivable tasks
Redefinition

27. AIHPE
Faculty
Curriculum
Design
Precision
Personalised
Remedial
Smart Content
Creation
Digital Resources
Interactive activities
Engagement
Tutoring
Feedback
Mentoring
Intelligent algorithm
Assessment
Automate &
Personalise
Higher order
learning
Self regulated
learning

28. Accessing Information/resources
Interface – Voice NLP HUD
Feedback
JIT coaching
Assessment –
diagnostics/formative/summative
Peer support
Portfolio
AIHPE
Learner

29. Data source for AIHPE
• Student profiling models and data
• student academic performance;
• attendance;
• participation;
• behaviour;
• consistency;
• resilience;
• persistence or ‘grit’;
• confidence or self-belief;
• enjoyment or engagement;
• emotional development;
• social development;
• ethical development;
• physical development.
• Learning analytics
• Historical
• Predictive

30. VR and AR in HPE
• Of anatomy/pathology
• Implants
• Instruments
• Equipment
VRDA 3D
Scan
• OR
• Ward
• Clinic
• ICU
• Imaging
VRSpace

31. AIHPE
• AI based content creation
• Video – Videobot, Magisto, Wevideo
• Writing - rytr.me
• NLP Transcriptions – Word/Google doc/otter.ai
• Research Writing – JBI Sumari/Ref-n –Write/NVIVO
• Automated Webinar - WebinarKit
vaikunthan@gmail.com
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