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Ai in Higher Education

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Presentation at Online Learning 2018/I4PL in Toronto, October 2018

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Ai in Higher Education

  1. 1. Using AI in Higher Education Stephen Murgatroyd, PhD Chief Innovation Officer, Contact North | Contact Nord
  2. 2. This Presentation Outline the Potential of AI for Education 1 Share Current Developments and Issues 2 Look at the Future 3
  3. 3. 10 Ways in Which AI Could Impact Higher Education “The future isn’t what it used to be…” Yogi Berra
  4. 4. 1. Natural Language Generation  Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.  Imagine in Education: Automatically generating assignment questions, course materials, teaching resources.
  5. 5. 2. Speech Recognition  Speech Recognition: Transcribe and transform human speech into a format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.  Imagine in Education: Being able to support learners who find following conversation difficult because of hearing difficulties; enabling the reconstruction of important conversations between researchers.
  6. 6. 3. Virtual Agents  Virtual Agents: From simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.  Imagine in Education: Expanding the use of chat-bots to link to adaptive learning engines so that students are able to be engaged in adaptive learning. They are also being used to provide 24/7 technical support and to help students navigate the administrative requirements of school, college and university programs.
  7. 7. 4. Machine Learning Platforms  Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree, 24[7].  Imagine in Education: A concrete example of machine learning in use is McGraw-Hill Education’s ALEKS, a web-based, intelligent assessment and learning system, which uses graph theory to break up a domain into concepts. The edges of the network might then be used as a bridge to another domain. For instance, if algebra has 500 concepts, a student might be tested when he or she begins learning, pinpointing exactly what a student knows and doesn’t know, and then creating an appropriate path through the learning domain that is selected based upon the start point and then continuously revising the learning map for that individual student.
  8. 8. 5. AI Optimized Hardware  AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI- oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia.  Imagine in Education: Faster simulations for science and technology programs and more accurate graphic and colour images for arts.
  9. 9. 6. Decision Management  Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.  Imagine in Education: Enabling critical decision making about the student journey – risk assessments (drop-out, non- completion, program switching, remediation requirements), providing instant supports “just in time”.
  10. 10. 7. Deep Learning Platforms  Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies.  Imagine in Education: Deep learning engines can generate learning pathways and resources to support specific learner needs, once the engine has mastered the learning outcomes expected of learners. A student struggling with a specific set of issues within a subject can have remediation from the AI system generated automatically so that the student can “get back on track” and master the knowledge and skills they need to progress.
  11. 11. 8. Biometrics  Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo.  Imagine in Education: Biometrics are already used as part of the remote proctoring systems for student exams and assessments; fingerprint, facial recognition and writing patterns. Fingerprints are used by some school systems as a way of automating payments, library borrowing and access to IT services.
  12. 12. 9. Robotic Process Automation  Robotic Process Automation (RPA): Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.  Imagine in Education: The University of Colorado Boulder is using robots fitted with telepresence screens to connect students temporarily unable to attend classes (due to illness, injury or some other reason) to their instructors and peers. Telepresence robots are remotely controlled video conferencing devices that are increasingly used in fields such as business and healthcare. Robots are also being used to help autistic students develop social and communication skills (Cheung, et.al, 2018; Van Hooijdonk, 2017).
  13. 13. 10. Text Analytics  Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.  Imagine in Education: The application of NLP in an education system is very effective for the analysis of errors in objective assessments and for the assessment of essays and complex answers to questions. Various linguistic approaches and tools can be utilized for analyzing the errors such as grammatical and stylistic errors, errors in fact or problems of understanding and competency. Teachers can easily mark these errors in the papers of students, but so can AI systems with a reliability of 92% when compared to human markers. Some 60,000 Chinese schools are using such systems for grading and marking assignments (Alger, 2018).
  14. 14. Current AI Activities in Higher Education Complete Summary at www.teachonline.ca
  15. 15. 1. Automated Feedback and Grading The Open University UK’s Open essayist To support students in drafting essays, a team from the Open University developed OpenEssayist, an intelligent linguistic analytics tool used in real-time to analyze text in essays and generate automated feedback. OpenEssayist analyzes 3 aspects of an essay; structure, key words/phases and key sentences and then presents a summary to the student that conveys the key points of the essay, allowing students to; 1) reflect on the draft text 2) review how the essay is organized 3) understand how the key terms are being used across the essay and how they combine to form a cohesive discussion. Evaluation of OpenEssayist shows the number of drafts submitted to the tool has a positive impact on the grades awarded for the first assignment. The cohort of students using OpenEssayist achieved significantly higher overall grades than the students in the previous cohort.
  16. 16. 2. Intelligent Tutoring @ The University of California, San Diego The University of California, San Diego, computer science Professor Pavel Pevzner and colleagues designed the MOOC course Introduction to Genomic Data Science as an adaptive Intelligent Tutoring System (ITS) for the edX platform. To lead students through a personal learning pathway, allowing evaluation at every stage of the student’s learning, the course has incorporated quizzes and “just in time” exercises to assess if the student has understood the content. If a student answers incorrectly, they are directed to a remedial site to assist with content understanding and attainment. The course also provides coding challenges to assess each student’s progress and to replace the basic multiple-choice quizzes prevalent in most MOOCs. Integrating an ITS into a MOOC format addresses the big issue of course modification and updating, as the ITS platform is designed to support easy, continual updating, especially useful in topics where students experience difficulty.
  17. 17. 3. Learning Analytics @ The Arab University (Kuwait) One of Arab Open University’s (AOU) main objectives is to double the number of students over the next 5 years. To accomplish this, they are using IBM Watson Analytics to improve student retention and identify students at risk of dropping out. IBM Watson Analytics uses artificial intelligence and machine learning algorithms to sense, predict, infer and provide recommendations. With insights revealed by the analytics, AOU can identify vulnerable students by pinpointing key drivers of student attrition. One of the most promising outcomes is the decision support dashboards, such as the one related to Student Risk Factor (SRF), a score which is composed of the student’s current GPA, progression rate, and the number of warnings received. By identifying at risk students, the dashboard acts as an early alert system, enabling the AOU management to take corrective actions and targeted initiatives to help struggling students get back on track for success — increasing retention and boosting student numbers.
  18. 18. 4. Student Support Services @ Deakin University Cognitive-computing technology, available 24/7, allows students at Deakin University to ask IBM Watson questions about administrative and course information in natural language in place of searching through keyword-based FAQs. Watson is asked over 1600 questions a week about an assortment of topics, such as admissions, enrollment, tuition and fees, financial assistance, student housing, extracurricular skills development, health and wellness, facilities, job placement, employment preparation, job skills assessment and academic help. Deakin University predicts the use of this tool will boost enrollment by up to 10% as a result of a 20% increase in student satisfaction.
  19. 19. 5. Adaptive Group Formation @ Universitat Politècnica de València (Spain) Higher education institutions are recognizing the pivotal role they play in developing essential teamwork skills among students to help prepare them for success in the workplace, using skills such as positive team dynamics, clear communication and interpersonal collaboration. The Universitat Politècnica de València in Spain addressed this need by developing a tool to create diverse, classroom-based teams grounded in theory that identifies eight different behavioural patterns and roles of successful teams. The tool calculates and proposes optimal team configurations and gathers feedback from team members on the roles of team members to be used in future team formation tasks. The team formation tool is more successful than traditional team methods in facilitating different teamwork aspects, such as student satisfaction, team dynamics, and cooperation and coordination
  20. 20. 6. Virtual Agents @ Georgia Tech A Georgia Tech computer science professor created a virtual teaching assistant (TA) named Jill Watson, based on the IBM Watson platform, to help answer the more than 10,000 forum posts in his online course of over 300 students. The virtual TA takes routine essential questions , such as queries about proper file formats, data usage, and the schedule of office hours - questions with firm, objective answers, while the human TAs handle the more complex questions. For the first few weeks, Jill Watson gave irrelevant answers which were posted to a site only the developers could see. They worked to overcome Jill’s issues and soon her answers had 97% certainty and they were sent directly to students with no intervention or vetting by developers. Students were unaware their TA was actually a computer.
  21. 21. 7. Virtual Reality @ Queens University (Canada) Ensuring students acquire comprehensive expertise with advanced theoretical knowledge and soft professional skills is a top priority for professional schools in law, engineering, medicine and business. This is why Queen’s University is using Ametros Learning’s intelligent simulations powered by IBM Watson’s cognitive-computing tool to focus on case-based teaching through simulations of real-world challenges, allowing students to develop and hone decision-making and problem- solving abilities. Students use textual, visual and oral communication on the platform to practice “real” communication interactions with artificially intelligent characters. These characters can take on the role of a client, vendor, patient, peer, and/or team member. Characters involve students in contextual interactions and provide individualized feedback on student work. The simulation platform creates a rich, risk-free simulated environment in their chosen field, where students learn through experience
  22. 22. 8.Personalized Adaptive Learning Environment @ University of Zagreb Spaced repetition is a learning technique that uses increasing intervals of time between learning new content and the review of previously learned content. It is usually applied in language learning to accommodate the large amount of content to be retained in the student’s long-term memory. Software can adapt to the student’s prior knowledge and use that information to help the student memorize characters, vocabulary, and phrases by determining how frequently the student must review content so it stays in their long-term memory. Faculty at the University of Zagreb harnessed this technique to teach Japanese to a group of 27 undergraduate students through the language learning application, Memrise. Students who regularly used Memrise for learning Japanese had 40% better grades than those who didn’t at the end of two consecutive semesters.
  23. 23. 9. Online Proctoring @ Stevens Institute of Technology  A team from Stevens Institute of Technology’s School of Engineering and Science piloted a virtual laboratory tool with biometric authentication used to identify the student and monitor their actions via remote proctoring using facial recognition techniques to an undergraduate mechanical engineering course. When using the virtual laboratory tool, the students log in by scanning their faces with a web camera. While performing a laboratory assessment, the student sits in front of the camera and the virtual laboratory tool monitors their facial expressions and head motions in order to identify suspicious behaviours. Upon detection of such behaviours, the tool records a video for further analysis by the laboratory administrator. The virtual lab tool with a virtual proctor works well and provides a high degree of accuracy in detecting suspicious behaviour during assessments.
  24. 24. Some Issues and Concerns
  25. 25. Key Concerns  Our inability to interrogate some AI systems to determine just how decisions within the system (outcomes) relate to inputs – what are the algorithms in use and how do they actually function? We call this the “explain-ability” challenge  Linked to this is the issue of bias – when data is being used to create algorithms, what kind of bias is implicit in the system? For example, it is already the case that a great deal of our understanding of coronary heart disease prior to 2000 was based largely (though not exclusively) on studies of men. It was assumed that our understanding of these data would apply equally to women and could be interpreted, with modest adjustments, for children and younger adults. Subsequent work has suggested that our prior understanding was limited by this bias. Data diversity is a key requirement for effective AI.
  26. 26.  Linked to this is the challenge of the “filter-bubble” – already an issue with social media. When social media filters information delivered to the user by past choices and decisions (e.g. Amazon book recommendations, Facebook tailoring feeds to suit patterns of choices made by the user), we begin to filter out messages, ideas, resources that do not fit the profile of the user. Vegans do not see meat recipes, fans of tango rarely hear ballet music.  There is also an accountability-responsibility problem. When a piece of software fails – for example, GPS software, it is possible to determine whether the problem is a hardware problem, a software problem, a data-entry problem or a human error problem. With AI systems, this is more difficult, since many of these sharp distinctions (software, data, human input) are blurred. When a self-driving car kills a pedestrian, for example, who is to blame? The system for making a choice between the driver’s safety and that of the pedestrian, the human in the vehicle, the pedestrian? Some have also referred to this as the “artificial stupidity problem” (Bossman, 2016).
  27. 27. and  Privacy is a key issue, especially given the huge appetite for data which AI systems appear to have. For example, there is the case of a retailer who was able to “know” that a teenage girl was pregnant before her parents did (Hill, 2012) or if unintended data-sharing between different parts of the UK’s National Health Service (NHS), breeching several policy and procedural guidelines (Hodson, 2016).  Bad actors are also a major and growing problem for AI systems. Despite many with good intentions, not all uses of AI are benevolent. There’s also the issue of bad actors, of both governmental and non-governmental nature, that might put artificial intelligence and machine learning to ill use. A very effective Russian face recognition system was deployed in 2017 and it proved to be a useful tool for an oppressive regime seeking to identify and crack-down on dissidents and protestors. Such a system could also be used to “out” a gay person, especially dangerous in those nations where being gay is a criminal offence. Another machine learning algorithm proved to be effective at peeking behind masked images and blurred pictures. Other implementations of AI and ML are making it possible to impersonate people by imitating their handwriting, voice, and conversation style, an unprecedented power that can be more than useful in a number of dark, criminal scenarios.
  28. 28.  The interface challenge – or managing the singularity is a substantial challenge. As more and more people live their lives in partnership with technology, how do we manage both the people:technology interface and the people:people interface? We have already seen major in social relationships (especially amongst iGen teens) – children now compete with technology the parents’ attention – as AI systems become sophisticated and we depend more and more on them, our relationship with each other and with technology is being challenged.
  29. 29. My Conclusion One task we each will have is to learn to dance with robots! We will all be cobots!