• May 15, 2018

Alelo Webinar Series on the Future of AI in Education and Training

Artificial intelligence is enabling dramatic improvements in education by providing learners with immersive personalized experiences, empowering teachers to be more effective, and giving administrators predictive analytics to achieve superior outcomes. AI also significantly increases access for learners and lowers costs, and helps overcome some of the most persistent skill gaps in the global workforce. When the education and training industry takes full advantage of this new technology the impact will be profound, as much as the adoption of compulsory education was in the 19th century.

To provide insight into the promise and potential of this new technology, Alelo offers a webinar series on the Future of AI in Education. We will look at the impact of AI on the experience of learners and teachers, the education and training industry, and the global economy. Seminar speakers will include Alelo experts as well as other thought leaders in the global community of artificial intelligence in education.


Webinar Schedule

The following is the initial webinar schedule. Alelo plans to add further seminars by international leaders in artificial intelligence in education in the coming months.

How AI is Solving the #1 Skill Gap in the Global Economy
Dr. W. Lewis Johnson
Wednesday, May 23, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, May 24, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Click here to listen to the recording

A recent analysis by LinkedIn of job openings reveals that communication skills are the number 1 skill gap in the US today. Artificial intelligence is now starting to be applied effectively to develop communication skills and other soft skills, at a lower cost than was previously possible. AI can objectively measure soft skills and promote rapid learning. This will lead to great benefits for workers and employers alike, with implications for the global economy.


Will Teachers be Replaced by Algorithms?
Dr. W. Lewis Johnson and Karen Chiang
Wednesday, June 6, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, June 7, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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Futurists claim that intelligent machines will replace teachers within 10 years. Should teachers really be worried? On the contrary, AI is more likely to empower teachers, reduce drudgery and overwork, and make their jobs more rewarding. We will present examples from experiences integrating AI into blended learning programs.


How Will AI and Data-Driven Learning Transform the Global Education Industry?
Dr. W. Lewis Johnson
Wednesday, July 11, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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Co-Sponsored by SIIA

AI is enabling a new data-driven approach to the design and delivery of instruction. Cloud-based AI tools automatically collect and analyze data from learners, making them analytics tools as much as learning tools. Machine-learning algorithms applied to learner data accelerate improvements in system performance. Teachers and administrators can use the resulting analytics to detect learner problems and intervene quickly. This webinar will describe how AI and data-driven learning are accelerating innovation, will help drive the transition from school-based learning to ubiquitous lifelong learning, and will fundamentally transform the global education industry.


Empowering Learning and Teaching Using AI
Dr. W. Lewis Johnson
Thursday August 2, 10:30-11:00 EDT / 7:30-8:00 PDT
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Co-Sponsored by IBM Cognitive Systems Institute Group

Artificial intelligence is empowering learners, by offering them opportunities to practice skills in a safe environment and personalized instruction focused on their specific learning needs. It can also empower teachers, by automating menial tasks and providing real-time analytics on learner performance. Together these innovations make possible systemic transformations to education.


How AI Research is Working to Support and Empower Educators
H. Chad Lane, Ph.D.
Wednesday, August 8, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, August 9, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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The potential of using technology to strengthen education was recognized in the early 20th century, and has since fueled decades of research on the design, development, and evaluation of technological innovations for learning. Researchers address questions that are derived from real, front-line problems in real educational settings, and that span the gamut of cognitive, motivational, emotional, and practical issues associated with teaching and learning. In this talk I will present an overview of the ways in which Artificial Intelligence research has contributed to this broader mission of strengthening and improving education. Although far from being comprehensive, I will introduce the theoretical and empirical aspects of AI in Education (AIED) research and discuss several examples of work that demonstrates a focus on supporting educators in the daunting challenges they face. I will pose the case against viewing AI as being on a path of “replacing” of educators (something I personally view as futile, even impossible in our lifetimes), and show that AIED researchers are seeking to build smarter, knowledge-based, and empirically driven tools that only enhance effective teaching and learning. Whether it be in our cars, homes, or offices, AI systems are consistently playing more prominent roles is all aspects of life, and so the key message of this talk will be that AIED researchers are tackling important problems with creative solutions meant to support and empower educators, and not supplant them.


Can AI Help Us Create More Inclusive Education and Society?
Dr. Kaśka Porayska-Pomsta
Wednesday, September 19, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, September 20, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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Artificial Intelligence (AI) has entered the mainstream of our lives. The increasing application of AI to diverse domains, from retail, to policing, healthcare, and education, has taken us to an inflection point, challenging us to make important decisions about how exactly we can build and utilise AI for the benefit of societal progress and wellbeing. This, in turn, offers us a unique opportunity to understand better what kind of society we represent now, and determine what society we aspire to create for the future. For example, AI has already proven to provide a powerful mirror onto the biases that are inherent in our social structures, systems and mindsets, highlighting social exclusion, disempowerment and inequality as areas of particular concern. Emergent evidence suggests that AI, in particular machine learning with its techno-centric implementation and use, can exacerbate those concerns. But can AI also serve to alleviate the inequalities, to cater for those who are at risk of exclusion from society, or for those who are deemed different from the so-called ‘norm’? In this webinar, I will examine these questions using examples of AI approaches to supporting human learning and development and educational practice, showing how AI can be utilised to enhance such practices and how it can help us create environments for socially enlightened and fair education, where diversity is embraced rather than punished, and where learners and teachers can become their own agents of change.


Beyond Academics: Intelligent Mentoring Systems for Career Success
Ran Liu
Wednesday, October 17, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, October 18, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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One of the ultimate goals of education is to effectively prepare students for long-term success. Most existing intelligent systems in education focus on adaptive tutoring for specific academic subjects and deliver personalized learning on a relatively short-term scale. Delivering sustained personalization of learning/mentorship for long-term success and aligning education to a rapidly-changing workforce remain lesser explored issues in artificial intelligence. I will discuss some of the unique challenges presented by these goals. These challenges include (1) the ability to track and integrate data from many disparate sources, at multiple grain sizes, and over long periods of time, (2) the need to adapt personalized learning models to consider longitudinal, cross-discipline, whole-person context rather than based strictly on within-tutor or within-session data, (3) the need to adapt models of engagement and motivation to consider longer-term trajectories and broader categories of behavior (for example, school attendance and discipline trajectories in addition to momentary estimates of affect and engagement), and (4) the real-time alignment of educational goals to the skills and knowledge needed in a rapidly-changing workforce. I will discuss promising approaches that can help us solve each of these challenges and move us closer to building effective intelligent mentoring systems.


When Teachers Orchestrate a Complex Lesson That Integrates Individual, Small-Group and Whole-Class Activities, How Can Technology Help Without Disrupting?
Kurt Vanlehn, Ph.D.
Wednesday, October 24, 12:30 PM-1:30 PM EDT / 9:30 AM-10:30 AM PDT
Thursday, October 25, 3:30 PM-4:30 PM EDT / 12:30 PM-1:30 PM PDT
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Technology has helped teachers with some of their daily tasks but not all. For example, intelligent tutoring systems can help with homework and tests, and classroom response systems (clickers) can help with lectures. Perhaps the next big application is helping teachers with classroom orchestration. Classroom orchestration refers to managing the flow of ideas and work products across individual, small-group and whole-class activities. As teachers walk around the classroom, they continually look for opportunities to improve students’ work. They act on the top priority opportunities. They may visit a group, ask a group to explain its work to the whole class, transition the whole class to a new activity, etc. The FACT (Formative Assessment with Computational Technology) project has developed a classroom orchestration system. It addresses two questions. (1) How can an orchestration system sense the state of the classroom without disrupting it? It should see even more opportunities for improvement than the teacher sees. It should not restrict the students’ freedom to work and collaborate. For example, it should not replace face-to-face spoken collaboration with typed chatting. (2) How can the system help the teacher handle more opportunities more effectively? The system should not increase the teachers’ cognitive load. The FACT system was iteratively developed over 52 trials in middle school math classrooms. Preliminary evaluations suggest that it succeeds in sensing the students’ work processes without disrupting them, and that it does not overload the teachers. However, the evaluations also found areas where teachers need even more help with classroom orchestration.


Data-Driven Development (D3) of Intelligent Learning Environments
Dr. W. Lewis Johnson
Wednesday, November 14, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, November 15, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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Cloud computing offers developers of learning environments access to unprecedented amounts of learner data. This makes possible data-driven development (D3) of learning environments. In the D3 approach the learning environment continually collects data from interactions with learners, which is used in ongoing evaluation and iterative development. Iterative development cycles become very rapid and more or less continuous, especially when machine-learning algorithms are applied to the incoming data to retrain the underlying models. This webinar will present some case studies of data-driven development and evaluation, and discuss the broader implications for the role of data in AI-driven learning environments.


Responding to the Whole Student: Student Attitude, Emotion and Behavior
Beverly Park Woolf, Ph.D., Ed.D.
Wednesday, November 28, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
Thursday, November 29, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT
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Artificial Intelligence In Education has begun to detect and respond to the whole student, including performance, attitudes, emotions and behavior. Fluctuating student emotions are related to larger, longer-term variables such as self-concept in mathematics. Students who used animated emotional companions indicated improved math value, self-concept and mastery orientation with reduced frustration. This webinar will describe how different online responses, such as responding with empathy, can improve learning and how computer vision can predict student emotion and behavior. Physiological activity, detected through sensors, predicts more than 80% of the variance of students’ emotional states. Finally, we will describe how to leverage big data to measure student learning and emotion and to support predictions about future events, e.g., college attendance and major.


Intelligent Narrative-Centered Learning Environments
James Lester, Ph.D.
Wednesday, December 12, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT Click Here to Register
Thursday, December 13, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT Click Here to Register

Adaptive learning technologies offer significant promise for bringing about fundamental improvements in education and training. For the past decade we have been investigating a family of intelligent game-based learning environments focusing on narrative-centered learning and integrating intelligent tutoring systems with game technologies. Research on these narrative-centered learning environments seeks to combine the inferential capabilities of user-adaptive systems and intelligent user interfaces with the rich gameplay supported by game engines. This line of investigation has the dual objectives of increasing learning effectiveness and promoting student engagement. In this talk we will introduce the principles motivating the design of narrative-centered learning environments, describe their roots in intelligent interactive narrative, and discuss ongoing work exploring their role in formal settings (K-12 schools, training) and informal settings.


Machine Learning and Human Intelligence: The Future of Education in the 21st Century
Rose Luckin, Ph.D.
Wednesday, January 9, 1:00 PM-2:00 PM EDT / 9:00 AM-11:00 AM PDT Click Here to Register
Thursday, January 10, 1:00 PM-2:00 PM EDT / 10:00 AM-11:00 AM PDT Click Here to Register

Description: TBD


Webinar Hosts

W. Lewis Johnson, Ph.D.
President and CEO, Alelo Inc.

Dr. Johnson is an internationally recognized expert in AI education. For his work on the first Alelo immersive game, Tactical Iraqi, he won DARPA’s Significant Technical Achievement Award. He has been a past President of the International AI in Education Society and was co-winner of the 2017 Autonomous Agents Influential Paper Award for his work in the field of pedagogical agents. He has been invited to speak at many international conferences such as the International Conference on Intelligent Tutoring Systems, and presented a Distinguished Lecture at the National Science Foundation.


Karen Chiang
Chief Revenue Officer, Alelo Inc.

Karen Chiang has been involved in language learning and testing in academic and workplace applications for over 30 years. Prior to working with Alelo she was VP of sales at Pearson and was responsible for talent solutions and commercialization of the Versant language assessment products which utilize automated scoring. Karen has experience in international and business development in emerging markets such as India.


H. Chad Lane, Ph.D.
Associate Professor of Educational Psychology and Informatics, University of Illinois, Urbana-Champaign

http://hchadlane.net/ @hchadlane
Prof. Lane’s research focuses on the design, use, and impacts of intelligent technologies for learning, engagement, and interest. This work involves blending techniques from the entertainment industry (that foster engagement) with those from artificial intelligence and intelligent tutoring systems (that promote learning). He has over 70 publications, delivered invited talks around the U.S and Europe, and has hands-on experiences in informal and formal learning contexts. His current research focuses on designing advanced learning technologies for informal learning.


Dr. Kaśka Porayska-Pomsta
Associate Professor of Adaptive Technologies for Learning and an RCUK Academic Fellow at the UCL Knowledge Lab

Kaska holds a Joint Honours Masters in Linguistics and Artificial Intelligence and a Ph.D. in Artificial Intelligence, both from the University of Edinburgh, UK. Her research is interdisciplinary in nature and focuses on developing adaptive interactive environments for learning and communication that are underpinned with real-time user and context modeling capabilities, especially in relation to users’ affective and motivational states. She is the Head of Research for the Department of Culture, Communication and Media at the UCL Institute of Education. She sits on the management committee for the Bloomsbury Centre for Educational Neuroscience, steering committee for the UCL Institute of Digital Health, and the executive board for the International Society for Artificial Intelligence in Education.


Kurt VanLehn, Ph.D.
Diane and Gary Tooker Chair for Effective Education in Science, Technology, Engineering and Math in the Ira A. Fulton Schools of Engineering at Arizona State University

He received a Ph.D. from MIT in 1983 in Computer Science, was a post-doc at BBN and Xerox PARC, joined the faculty of Carnegie-Mellon University in 1985, moved to the University of Pittsburgh in 1990 and joined ASU in 2008. He founded and co-directed two large NSF research centers (Circle; the Pittsburgh Science of Learning Center). He has published over 175 peer-reviewed publications, is a fellow in the Cognitive Science Society, and is on the editorial boards of Cognition and Instruction and the International Journal of Artificial Intelligence in Education. Dr. VanLehn’s research focuses on intelligent tutoring systems, classroom orchestration systems, and other intelligent interactive instructional technology.


Ran Liu
Chief Data Scientist at MARi

Ran Liu is a career- and whole-person-oriented intelligent mentoring and skill tracking platform. Prior to working at MARi, Ran completed her Ph.D. at Carnegie Mellon University, supported by a National Science Foundation fellowship. In her dissertation work, she developed video games to improve non-native language learning and measured their impact on transfer to real-world non-native language tasks. Ran also completed her post-doctoral training at Carnegie Mellon University working on educational data science research. Her post-doctoral research focused on advancing intelligent learner models as well as testing the effect of such modeling advancements on classroom learning outcomes.


Beverly Park Woolf, Ph.D., Ed.D.
Research Professor in the College of Information and Computer Sciences, UMass-Amherst

Dr. Woolf develops intelligent tutors that model a student’s affective and cognitive characteristics and combine an analysis of learning with artificial intelligence, network technology, and multimedia. She published the book Building Intelligent Interactive Tutors along with over 250 articles. She is the lead author on the NSF report Roadmap to Education Technology in which forty experts and visionaries identified the next big computing ideas for education technology and developed a vision of how technology can incorporate deeper knowledge about human cognition.


James Lester, Ph.D.
Director of the Center for Educational Informatics and Distinguished Professor, North Carolina State University

His research centers on adaptive learning technologies that utilize AI to create learning experiences that are designed to be both highly effective and highly engaging. Over the past decade, his work has focused on intelligent game-based learning environments, computational models of narrative, affective computing, and natural language tutorial dialogue. The adaptive learning environments he and his colleagues develop have been used by thousands of students in K-12 classrooms throughout the US. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).


Rose Luckin, Ph.D.
Professor of Learner Centred Design, Institute of Education, University College London

Rose Luckin is Professor of Learner Centred Design at UCL Knowledge Lab in London. Her research involves the design and evaluation of educational technology using theories from the learning sciences and techniques from Artificial Intelligence (AI). She has a particular interest in using AI to open up the ‘black box’ of learning to show teachers and students the detail of their progress intellectually, emotionally and socially.