IAFOR Journal of Education

Volume 6 – Issue 3 – Winter 2018

Artificial Intelligence and the Student Experience: An Institutional Perspective
Kriti Khare
University of Alberta, Canada
Brian Stewart
University of Eastern Finland, Finland
Anshuman Khare
Athabasca University, Canada

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Abstract
The paper outlines the potential for Artificial Intelligence (AI) to positively impact student
success. This will be approached from a student life-cycle perspective, taking an integrated
view of the student experience and identifying where AI can be most beneficial. Current usagesof AI in education will be considered, in addition to those being experimented with and thosestill being considered. The paper will view the adoption of AI in education from acomprehensive perspective, considering technological, social, political, economic, cultural andethical factors, providing a frame for understanding of the benefits and constraints of the mostintelligent of information technology in the educational realm.
AI has started to emerge in educational institutions in the form of chat bots that are being used
to provide student services as well as providing learning supports. Automated paper grading
has started to be used, while academic advising and assessment are being trialed.
Keywords: higher education, artificial intelligence, Student Experience Practitioner Transitions
frameworks

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Artificial Intelligence (AI) has been increasing in its use in our everyday lives spanning a broad
swath of uses ranging from personal assistants, purchase reference and prediction, smart
homes and cars, fraud detection, online customer support, and even assisting personal
relationships. This increasing use is fueled by the use of machine learning, computer modelling,
and algorithmic creation enabled by ever bigger data sets combined with ever more capable
technological capabilities driven by Moore's Law (Schaller, 1995) and Metcalfe's Law(Hendler & Golbeck, 2008).
The upward and accelerating trajectory of AI, encapsulated in the concept of thesingularity, has drawn both excitement and concern from scientists, economists, and politicaland business leaders. The largest fear is that AI will outsmart its creators allowing the machines
to turn the tables and become the masters, using our psychology to program our behavior.
Further disquiet exists with respect to ethical considerations (Moore, 2006), governance ofappropriate usage (Khatri & Brown, 2010) and to instances where programming bias have beenshown to exist in early deployments of the technology (Devlin, 2017). These concerns are valid
and remain to be addressed, however it is not our intention to pursue these here. We are viewing
real applications of AI to education that are practical and achievable in the near term. More
broad-based sociopolitical and economic issues are not discussed in this paper. Nor are
implications for the curriculum and the almost certain requirement for the incorporation of AI
literacy and information accuracy into all disciplines, lest intellectual laziness yield anunverified trust to systems that were based on their creators’ assumptions.
The student higher education experience can be considered as a series of interdependent,
overlapping, but not necessarily sequential, phases. This life-cycle approach is often used byadministrators to manage student life as it distinguishes the critical elements of experience
allowing the design and delivery of focused administrative services. The student lifecycle inhigher education is defined as the journey of the student from first contact with an institution
through to becoming an alumnus. The ultimate goal of a student is academic achievement
accompanied by self-development through the academic experience. The academic success ofstudents, however, relies on a composite of all aspects of the student’s life. These other aspects
include mental welfare and support, social interactions, sports and physical health, effective
life balance, all of which contribute to the experience the student has in their higher education
career (Morgan, 2013).
Applying a technology into a complex environment, particularly one as traditional as higher
education, is a very challenging endeavour. As with many technologies, the key question iswhere to start, what use case would provide a fair test of the technologies capabilities? The
purpose of this paper is to address this by providing an approach for the coherent adoption of
AI into higher education institutions to lessen both the cost and time for its benefits to beavailable. The use of the student lifecycle and the grouping of activity sets creates target groups
for experimentation and piloting within definable and accepted domains, allowing for effective
hypothesis testing, collaboration and comparison with other institutions. While not wishing tounderestimate the degree of difficulty such a shift may incur, it is reasonable to suggest thatsuch an approach will improve the rates of early adoption and the speed to production.
One model developed to use this framework to understand the student journey, outlining thedifferent stages that a student transitions through during their academic career, is the Student
Experience Practitioner Transitions (SEPT). The model was developed to educate and guidepractitioners about the various kinds of supports students need at each stage (Morgan, 2013).

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In this article, using the Student Experience Practitioner Transitions (SEPT) model developedby Morgan (2013) as a basis, five potential areas in the process where artificially intelligent
systems can be incorporated are analyzed (Figure 1). The functionality that these systems will
perform, the tasks they would take over from the professors, teaching assistants and support
staff, as well as related research, are discussed.

Figure 1. The Student Experience Practitioner Transitions (SEPT) framework (Source:
Morgan, 2018)
The Student Experience Practitioner Transitions model has six stages of student life, each stage
examining the unique position of a student at a certain point of time in his or her higher
education (Morgan, 2013). The stages can be summarized as follows:
1. First Contact and Admissions – At this stage, the student makes an application to an
institution based on his/her interests.
2. Pre-arrival – The student has been accepted to a program and any other requirements
must be completed before the student arrives to embark on the course of study, such asreceipt of final transcripts.
3. Arrival and orientation – Introduction to how the degree will be completed and gettingused to campus life.
4. Induction to study – The first year of education when the student is introduced tocoursework.
5. Reorientation and induction – gaining new skills as one progresses through their degree
by taking advanced courses.
6. Outduction – entering the job market.
By viewing the student experience as a journey consisting of integrated steps one can use AI
to analyse the steps and determine interdependencies between them to develop an integrated
model of student behaviour. This model can greatly assist the understanding of behavioural
determinants that impact students throughout their lifecycle. It further provides a classification
for data sets that are available for model and algorithmic development.

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The Artificial Intelligence Process
AI is a broad field that is comprised of many disciplines including computer science, statistics,
linguistics, psychology, and decision science. It is essentially concerned with getting acomputer to replace human intelligence in assigned tasks. Given the breadth of the field it is
not surprising that there are quite a few definitions of AI. In addition these are non-constant as
the capabilities develop. What was once considered AI begins to be seen as algorithmic
development or big data analytics. A commonly accepted breakdown is to view AI as theoverarching rubric which encompasses Machine Learning, which further encompasses Deep
Learning. Rich and Knight (1991) state that “Artificial Intelligence (AI) is the study of how tomake computers do things which, at the moment, people do better” (p. 3).
The definition of Artificial Intelligence, as stated in the first Volume of the Handbook of
Artificial Intelligence is that “Artificial Intelligence (AI) is the part of computer science
concerned with designing intelligent computer systems, that is, systems that exhibit thecharacteristics we associate with intelligence in human behavior - understanding language,
learning, reasoning, solving problems, and so on” (Barr & Feigenbaum, 1981, p. 3). Thisdefinition is appealing, as in this paper we are imagining a system that can correlate data fromdifferent sources and present options and pathways to students based on their interests andeligibility, similar to a human counsellor. For the propose of this paper we take AI’s meaning
in the broadest sense, any use of a computer to replicate or substitute human intelligence toprovide insights through the application of various machine enabled analytical processes tolarge data sets. Insights from Artificial Intelligence are only possible when data is available
related to the sought for insight. This data may be collected by surveying people, gathered frompeople completing tasks, automatically generated and stored by a system in log files, enteredin by an analyst, etc. Data may be structured, always in a particular format e.g. form entereddata; semi structured, complying to a structure e.g. emails; or unstructured, where it does notcomply to a given structure e.g. photographs. The latter two require reprocessing to be usable.
After looking through all the data that is available and identifying the sources that would be
helpful, this data has to be transferred and stored in a database or on a server, making it
available in a format that the AI algorithm can process. Once the algorithm processes the data,
insights can be obtained. Figure 2 describes a very simple and generic model: Data Generation– Data Storage – Data Processing – Actionable Insights.

Figure 2. A simple AI process
Based on this Input-Process-Output model, data has to be collected and made available in aform for each stage in the Student Lifecycle, that the AI program can process and then yield67

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insights that the student and organization can act upon. In the next sections, possible places
where AI insights would be helpful are reviewed and a reference grid shows a high-level data
to lifecycle stage mapping, articulating the data needed and the potential constraints andbenefits of applying AI at each stage.
Incorporation of Artificial Intelligence at the Classroom Level
The mechanics have to be explained here and then the experience. The student would firstinteract with the system and input their interests, performance in studies to date, work
experience, amongst other information. The intelligent system would then be able to provide alisting of the programs that the student is eligible for at the institute. If this system is utilizedby more than one institution, programs across institutions might be suggested to the student as
well. Possible pieces of information that could be utilized to train such a system could be theinformation from current students who are pursuing a program at the institution, their interests,
the programs that they might have considered before pursuing the one they are in as well as
career prospects. Similar data may be collected from alumni, noting the career that they are
pursuing.
Once the student decides on a program of study and is accepted, the system would be able toshow the student possible scholarships, volunteer opportunities, as well as present program
specific information about preparing for the first day, book a tour of the campus, residence
services, library services, potential student clubs (based on the interests that the system isalready aware of), time scheduling, and so on. This would potentially cover the second andthird stages of SEPT.
After classes have been selected and a term has been successfully completed, the system would
be able to offer more refined job and volunteer opportunities based on the skills that the student
has learned as a result of this education. Sweeney, Lester and Rangwala (2015) and Sweeney,
Rangwala, Lester and Johri (2016) cited in Khare, Lam and Khare (2018) “predict whether the
combination of courses that a student is taking in the current term would overwhelm thestudent. Thus, their research gives insight to students about courses they are taking, tocounselors who advise the students about the course load and to instructors on considering
differing course combinations” (p.43).
This would be an intelligent system that knows what the student is studying in all courses, thedeadlines coming up as well the next set of courses that would become open to the student ifs/he does well in his/her current set. This would integrate data from the various courses and the
database of course dependencies to show the student possible what-if schedules for next term
and year, one that can be changed according to interests and constraints facing the student. The
system would also be able to analyze integrated data from the numerous sources to present thestudent his/her best options (Woolf, Lane, Chaudhri, & Kolodner, 2013). This also relates tothe field of learning analytics and student competences and skills. Zhang and King (2016)
analyse the order in which questions should be presented based on the knowledge level of thestudents. This can be further applied to the skills that courses teach and other courses require.
By keeping track of the time commitments of the student based on selected courses, volunteer
work and part-time job, the student would have a more comprehensive idea of how their time
is being spent and if they can take on more work. Woolf et al (2013) elaborate on the 21st
century skills that artificial intelligence should work to address. These include self-direction
and self-assessment. With information organized in a coherent way and presented such that68

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only eligible options are shown, a student would be in better control of making choices thatlead to the success he desires.
Intelligent systems make it possible for students to build a schedule for the term, while at thesame time presenting financial aid and volunteer opportunities tailored to them for which they
are eligible. This would reduce the number of human hours spent in counselling students
regarding the courses they are eligible for as well as reviewing scholarship applications because
the students would only be able to apply for the ones they are eligible for.
The Outduction of a student at the end of their course of study requires students to make achoice. Some students will seek to continue studying by taking another degree or pursuing an
advanced degree. AI systems can provide guidance from their academic record as to a preferredcourse of study. Most students will want to move on to the workforce to start their careers. AI
can assist students by providing career tools to align their aspirations with the pathways to getthere. Further AI career coaches can provide personalized advice based on the student’s history,
experience, locational choice, skills combined with career requirements to supply students
needed further study tracks and possible staging and development paths. IBM’s AI powered
Blue Matching system is an example of a job matching service that demonstrates the realpotential here (Clegg, 2017).
All of the above are dependent on available and useable data. This is not a trivial requirement
as most of the data will not be in a directly useable form and will need either significant
reworking or will require to be newly gathered in the desired format. Machines, like humans,need information to learn from, they also need, at least with the present state of technology,
humans to shape that data for them. While quicker when they have the appropriate data,
available AI systems have yet to be able to effectively forage on their own behalf.
Forum Monitoring by Intelligence Systems
Forum monitoring offers another opportunity for AITAs to improve the efficacy ofcollaborative forums. Currently human Teaching Assistants (TAs) are asked to check the forum
at least once a day such that all questions could be answered within 24 hours. The same rules
are applied to emails and TAs are encouraged to ask the students to post a question on theforum if its answer would be helpful to other students.
Questions are often related to the concepts being studied in the class and the assignment. Thus,
the scope of the questions is often defined. Using data from forum posts from previous
iterations of the course, machine learning can be applied and trained to associate questions withanswers. This reduces both response time (or eliminates it) and the effort of TA’s to researchand create an answer. If a set of resources are associated with each topic, it is possible to pointout these resources to students, just like the TA would have done (Khare, Lam, & Khare, 2018).
Algorithms can be used to time themselves for answering the student and if no answer is foundor it is taking too long, a mechanism can be put in to notify a teaching assistant. Once the TA
resolves the question, the algorithm may be trained further to answer it next time or to flag
certain questions that must go to TA directly. Going beyond answering questions, reviewing
the forum posts for the understanding of the students, using content analysis and text mining
techniques can determine if the discussion coverage is as expected (Khare, Lam, & Khare,2018).

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Tutoring and Advising
The use of intelligent agents and chatbots are growing rapidly in use in consumer electronics
and customer service. Commonly referred to as chatbots these devices use AI to provide
context aware information to the user, usually in a relatively narrow range. The application ofthis technology to support students enables the potential for expanded one-on-one tutor
engagement, not economically achievable in existing teaching models. These are being trialledin education, varying from providing administrative and service information to supporting
academic study. A number of studies suggest that chatbots, also known as intelligent tutors or
intelligent teaching assistants, are significantly beneficial to students and positively add to thestudent experience and probability of success. IBM Watson supported chatbots are being usedin Deakin University (Deakin University, 2015) in Australia to provide student guidance to lifeon the campus and in the cloud, and at GeorgiaTech’s online master’s in computer science toprovide teaching assistance. Both have proven very successful after initial training, due in largepart to the question set remaining relatively consistent allowing AI to prove effective (Maderer,2016). Georgia State University noticed that not all students who accept admission offers enroll
in Fall. They called this phenomenon “summer melt” (Ravipati, 2017). They employed an AI
chatbot called AdmitHub to significantly increase the number of students that enroll after
admission by improving communication with students using text messages (Ravipati, 2017).
Studies by Steenbergen-Hu and Cooper (2013; 2014) and VanLehn, (2011) have indicated that
AI tutors are as good or better than human tutors, however the evidence is unclear, and theexperimental designs used to date do not provide an unambiguous answer. Moreover, humanto human interaction for teaching is unlikely to be replaced in the near term. As humans, we
still need personal connections for inspiration, compassion, self-reflection, imagination and life
context. Learning is every bit as much emotional and social as it is teaching technique andtechnology. The replication of intelligence by machines may not be matched by their abilities
to emote or socialize, indeed the concept of artificial emotion seems to be a contradiction interms. Thus, the ability of humans to express empathy and to provide emotional as well as
intellectual understanding to form connections and form social bonds will ensure, at least for
the near future, human advising and tutoring are superior supports for human learning.
Grading and Assessments
Many aspects of grading automation are currently in practice and are well accepted within thehigher education community. In many instances logical rules and rubrics are used for grading
and these can be taught to a program, such would result in a reduction in the number of human
hours spent on grading. These are, in the main, straightforward tabulations of choice typequestions, where there is a predetermined correct answer. Assessments and grading of lessdiscrete answer sets, in particular long essay questions, are not likely to receive as warm awelcome by academics. Nonetheless AI is being applied to the grading of short and long essay
types and is showing considerable success. The work done in this field includes automatic
feedback. Zhang, Shah and Chi (2016) have worked on automatically grading short answer
questions. Automatic Essay Grading is a growing field of research that aims to grade long essay
questions (Page, 1994; Rudner & Liang, 2002; Chen, Liu, Lee, & Chang, 2010; Dong & Zhang,2016). Recent work by Dong and Zhang (2016) elaborates the potential of deep learningalgorithms to grade essays.
Christian (2018) formulated a rule-based system to evaluate C++ programs in the early stages
when a student is learning a new concept by grading and informing the student on where he iswith respect to the solution and learning objective.

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It is quite likely to see AI aided formative assessment technology being employed as this aids
academic productivity and assists students in their development of understanding and masteryof the material. Chen, Breslow and DeBoer (2018) analyzed a blended learning environment
and the effects of immediate corrective feedback on student behavior. They found that the
feedback led to reflective studying, and higher performance was predicted for students who
used the corrective feedback feature.
Delivering content based on the performance or understanding of the student
Adaptive learning has been much trumpeted over the years and was seen as one of the early
benefits of online or computerised enabled learning. The use of games in education follows asimilar pedagogical approach of uncovering within a dependent interrelated environment ofknowledge objects or situations. Squire and Jenkins (2003) discuss the pedagogical potentialof games. Polin (2018) elaborates upon the features of games that make them spaces that
support learning. Lamb, Annetta, Firestone and Etopio (2018) provide insights into the kind ofgames – Serious Games, Educational Serious Games and Educational Simulations that havethe most impact on students’ cognition and behavior. However, the recent troubles of Knewton,
one of the flagship companies in this area (Young, 2017), indicates that this is still an area thathas yet to fulfil its potential.
Self-paced learning is where the program can judge when a new topic has to be introduced oran older topic has to be reviewed by the student. The models used by Intelligent tutoring
systems would be helpful here to determine when a student has learned a concept and is readyto move on to the next one (Lin & Chi, 2016; David, Segal, & Gal, 2016). The data fromassignments and practice questions as well as response time is often used to find the state of‘learned’ and build a student model which represents the knowledge of students (Lin, Shen, &Chi, 2016). These systems provide feedback, timely guidance and explanations when students
make mistakes (Shute, 2008). They keep track of the learning outcomes and are able todetermine the content appropriate to the student’s difficulty level (VanLehn, 2006). In this way,
students’ learning experience has a bigger focus than the lessons themselves.
Intelligent Tutoring Systems such as Carnegie Learning and Front Row have been tailored toschool students. To the best of our knowledge, there are no cognitive tutors at university level.
These could be used to supplement the understanding of students.
Augmenting this information about the student with ‘smart content’, Cram101, built by Content
Technologies Inc., uses artificial intelligence to breakdown the textbook into smaller sections,
including chapter summaries, practice questions and flashcards to form a ‘digestible “smart”
study guide’ (Faggella, 2017).
AI applied to the classroom level can provide significant benefits which are even more tellingin the online environment where the delivery of courses can be enhanced economically throughAI enabled automation. The ability to provide responsive support to students on a consistentbasis, although inferior to human interaction, is nonetheless superior to current online delivery
models that normally have time limited support due to the cost of human TAs. Even moderatequality support that is consistently available is preferable to high quality support that is rarely
available. Moreover, a human TA or professor can review and provide additional support to
the AITA whether due to AI restrictions or by providing additional learning moments. The
potential impacts on the online business model are substantial, by reducing the total cost ofproviding online courses, a significant lowering of tuition to students is possible. Such would
allow greater access to secondary and tertiary education particularly in less economically71

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Volume 6 – Issue 3 – Winter 2018advanced countries. Slightly ironic that artificial intelligence may be the key to growing global
human intelligence.
Combining Artificial Intelligence with the SEPT Model
There is an iterative loop here as data generates insight creating the need for additional datathat generates enhanced insight. It also can demonstrate the gaps in data for a given desired
insight as well as potential insights from given data sources. By doing this we can show thepotential for AI to provide benefit to learning and student success. Morgan (2013) identified
five themes in the SEPT model – curriculum and assessment, pedagogy, support, finance, andemployment. Table 1 is based on the data available from these themes and presents a further
analysis of the data sources, possible insights, and barriers to using the data.
Table 1. Artificial Intelligence (AI) Lifecycle Matrix using SEPT framework
Question\StageWhat data
is available
for each
stage

What arethe sourcesof this dataWhatsystems
does theinformation
reside inFirst
Contact andAdmissions
Student
interests
Subjects
taken by
student
Grades in
subjects
Degreeapplied for
Courses tobe taken in
the degreeFeeestimates
Possiblescholarships
and financial
aid
Part-time
workopportunities
Student
application
Referenceletters for the
student
Previous
examination
results
Careerdevelopment
system at theinstitution

Pre-arrival

Arrival andorientation

Induction to
study

Reorientationand induction

Outduction

Subjects
selected by
student tostudy –
coursework
Moneymanagement
adviceFinancialsupportavailable
Placement
options
Internships
Volunteering
opportunities

Subjects thatmake up the
degreeAssessments
Moneymanagement
adviceFinancialsupportavailable
Placement
options
Internships
Volunteering
opportunities

Subjects
being
studiedAssessments
Moneymanagement
adviceFinancialsupportavailable
Placement
options
Internships
Volunteering
opportunities

Skills gained after
a term/year of
study
Subject choices
available
Moneymanagement
adviceFinancial support
available
Placement options
Internships
Volunteering
opportunities
Future job
opportunities
Professional
development
opportunities

Job
opportunities
Further study
prospects
Debt control
Professional
development
opportunities
Transitioning
into aworkspace

Student
profileFinancialprofile ofstudent
Scholarships
applied forand rewards
Careerdevelopment
system at theinstitution
Student
Information
SystemCustomer
RelationshipStudent
profileFinancialprofile ofstudent
Scholarships
applied forand rewards
Careerdevelopment
system at theinstitution
Student
Information
SystemCustomer
RelationshipStudent
profileFinancialprofile ofstudent
Scholarships
applied forand rewards
Careerdevelopment
system at theinstitution
Student
Information
SystemLearning
outcomes andcompetencies ofprevious courses
Job profiles ofgraduates fromselected degree
Careerdevelopment
system at theinstitution

Requirementsof graduate
degrees
student iseligible for
Job profilesof graduatesfrom selected
degreeCareer plans

Student
Information
SystemFaculty databases

Student
Information
System72

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Question\StageWhat
Insights
can wedevelop foreach stage

First
Contact andAdmissions

Degrees andCourses thatmatch andbest fit
student’s
interest andlikelysuccess
What-if
Financialscenarios
Optimal path
to complete
degreeVolume 6 – Issue 3 – Winter 2018

Pre-arrival

Arrival andorientation

Induction to
study

Reorientationand induction

Outduction

Management
SystemFaculty
databases

Management
SystemFacultysystems
Residential
database
Student
union
systems

Residential
database
Learning
management
systemStudent union
databases
Library systems
Physical activity
systems
Volunteer systems

Faculty
databases
Residential
database
Learning
management
systemStudent union
databases
Careerdevelopment
systemVolunteering
choiceOptimalschedule tomanagecoursework,
work andvolunteering
Steps to taketo besuccessful inclasses
Careeropportunities

Time
management
Subjects to
takeCampusOrientation

Faculty
databases
Residential
systems
Learning
management
systemStudent
union
databases
Librarysystems
Volunteersystems
Optimalstudy plan
Resource
suggestions
andprovision
Study aides
and course
strategies
Practice
assignments
Dailyschedulemanagement

Optimal study plan
Resource
provision
Study aides andcourse strategies
Practice
assignments
Schedule tomanagecoursework, workand volunteering
Time management
Careeropportunities
Job shadowing
opportunities
Projects/Workshop
suggestions thatwould strengthen
job prospects later
Skills that areunderdeveloped orneed improvement
Learning progressand successsupports

Best fit Jobs
to apply for
Further job
specific
training
opportunities
Application
deadlines forgraduate
programs
Best fit
International
study
opportunities

Residential
choiceTimemanagement

Whatbarriers tousage andvalue exist

Privacy of student information
Maturity of IT systems
Data accuracy, availability and accessInstitutional culture and policies
Lack of systems integration
Lack of data sharing agreements with external organisations
Lack of appropriate data
Lack of understanding of AI

What
enablers tousage andvalue exist

Data governance to develop and ensure consistent data architecture in combination withstandardized data definitions
Identification of additional key data sources that bear on student experience
Policy review of data sharing to facilitate and frictionless accessIntegration of data and applications to provide real time data across the enterprise73

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Question\StageWhere canit go next

First
Contact andAdmissions
Enhancedengagementof potential
students
through
intelligent
selection andinformation
provision to
selectinstitutions,programmes
and career
paths

Volume 6 – Issue 3 – Winter 2018

Pre-arrival

Arrival andorientation

Induction to
study

Reorientationand induction

Outduction

Augmented
reality
campusguide
Campus life
planner
Residential
plannerSocial life
assistants

Data sharing
across theinstitution toprovideinsights into
success
patterns
Learning
materials
agent toreduce costs

Real time
data
availability
for improved
decisionmaking on
programmes
and course
selection

Study buddy
agents
Virtual andaugment reality
learning resources

Integration
with work
placement
dbases toprovide bestfit
opportunities
for job
placement
and careeradvisement

Conclusion
The two main bodies of systems analyzed in this paper were those of the student lifecycle andthe learning management system. While the lifecycle management systems keep track ofoverall student progress, the courses they have taken, venues of financial support, volunteer
opportunities and schedule management, the learning management systems are focused on theacademic progress of the student. From this it is seen that student lifecycle management relies
more on the administrative staff while learning management systems are supervised by theacademic staff at a higher education institute.
As with all innovations their adoption is not an either-or, but a blending and integration of thestrengths of both the existing and the new to provide a superior capability. If we, therefore,
assume that we can combine activities currently managed by these systems with an intelligent
system, we can begin to redesign a new system of education, one breaking out of the traditionalroles of professors, teaching assistants and support staff, making them facilitators of knowledge
and managers of the new systems. At the classroom level, in many cases, it will free up time
to use the in-class time in a different way. The concept of flipped classrooms is where students
study the assigned material at home and come to the class to discuss it. This might be
supplemented with learning activities in class such as discussing complex concepts or
examining real-world examples. On the other hand, at an administrative level, work of support
staff may be reduced with regards to providing counselling to the student about potential next
steps as the student would already have access to the courses they are eligible for, credits they
can take from other universities, the career venues and graduate programs open to them basedon his/her current performance and volunteer opportunities to augment his/her resume, to namea few areas. The advantages of such an integration of the student lifecycle and learning
management systems is to give students the choices and options, right for them, at the righttime in their higher education journey.
Another system using artificial intelligence which we did not explore here is at the academic
department and faculty level for curriculum design. Such a system would analyze the needs ofthe discipline, trends in the current job market and research as well as the new knowledge skills
that graduates require for being successful. This information would feed into design anddevelopment of new courses and programs, enhancing student experience by increasing theiremployability.

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This paper has attempted to show that AI is and can be a significant aid to all aspects of thestudent experience and to the organisations, structures, processes and people that make up
educational systems. It further provides an architectural approach that is a coherent
representation of real experience, which provides a context for experimentation and thedevelopment of a referenceable literature. It does not try to make the case that AI is superior
or equal to human equivalents, rather it attempts to demonstrate the benefit of synergistic
integration of both forms of support for student success, allowing each to support the other toprovide what they are best at. By so doing all stakeholders benefit and the student experienceis improved with the expectation that student success increases pari passu.
Finally, we need to be aware of techno-solutionism to address wickedly complex problems,
but we should also explore its capabilities to find the best-fit for its application and benefit to
an increasingly expensive and technology resistant system. The pace and depth of adoption
will depend not only on the continued growth of AI capabilities, but also on the opening andsharing of data and the acceptance of a highly conservative system unused to collectively
integrating technology into its corpus. By providing an adoption framework this paper desires
to enable a more structured and efficient introduction of AI technology into Higher Education.
One that can benefit students, faculty and administrators through an enhanced user experiencethat improves the ability of educational institutions to deliver on their core mission of teaching,learning and research.

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Corresponding author: Anshuman Khare
Contact email: Anshuman.khare@fb.athabascau.ca

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