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

This webinar series looks at the impact of AI on the experience of learners and teachers, now and in the future, and considers the impact on the global education and training industry and the global economy. Webinar speakers include Alelo experts and other thought leaders in the global community of artificial intelligence in education.


Upcoming Webinars


Past Webinars

#1 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 Eastern / 10:00 AM-11:00 AM Pacific
Thursday, May 24, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#2 Will Teachers be Replaced by Algorithms?
Dr. W. Lewis Johnson and Karen Chiang
Wednesday, June 6, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, June 7, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#3 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 Eastern / 10:00 AM-11:00 AM Pacific
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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.

#4 Empowering Learning and Teaching Using AI
Dr. W. Lewis Johnson
Thursday August 2, 10:30-11:00 Eastern / 7:30-8:00 Pacific
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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.

#5 How AI Research is Working to Support and Empower Educators
H. Chad Lane, Ph.D.
Wednesday, August 8, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, August 9, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#6 Can AI Help Us Create More Inclusive Education and Society?
Dr. Kaśka Porayska-Pomsta
Wednesday, September 19, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, September 20, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#7 Beyond Academics: Intelligent Mentoring Systems for Career Success
Ran Liu
Wednesday, October 17, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, October 18, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#8 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 Eastern / 9:30 AM-10:30 AM Pacific
Thursday, October 25, 3:30 PM-4:30 PM Eastern / 12:30 PM-1:30 PM Pacific
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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.

#9 Data-Driven Development (D3) of Intelligent Learning Environments
Dr. W. Lewis Johnson
Wednesday, November 14, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, November 15, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#10 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 Eastern / 10:00 AM-11:00 AM Pacific
Thursday, November 29, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#11 Intelligent Narrative-Centered Learning Environments
James Lester, Ph.D.
Wednesday, December 12, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, December 13, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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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.

#12 Improving Comprehension Strategies of Struggling Adult Readers Through Conversational Trialogues With AutoTutor
Art Graesser, Ph.D.
Wednesday, February 20, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, February 21, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Download the presentation file
Questions and Answers
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One in six adults in the United States has low literacy skills and face difficulties with daily literacy tasks. These concerns prompted the Institute of Education Sciences to fund the Center for the Study of Adult Literacy, a research center that focuses on adult literacy for adults who read between grades 3 to 8. This presentation describes how conversation-based computer agents can help them improve their comprehension skills by holding conversations in natural language. The AutoTutor system on the web implements three-party conversations, called trialogues, where two agents (such as a tutor and a peer) interact with the adult. AutoTutor trains comprehension strategies in 35 lessons that target the theoretical levels of words, the meaning of the explicit text, the situation model with inferences, rhetorical structures, and digital technologies. Data mining procedures have been pursued in detecting different clusters of readers and the adults’ engagement (versus disengagement) in the AutoTutor interaction, based on the accuracy and response times to questions that the conversational agents ask the adults. Improvements in comprehension were found on three different psychometric tests for three out of four clusters of readers.

#13 From AI-Enabled Computer Supported Collaborative Learning in the Classroom Towards Team-Based Learning in the Workplace
Dr. Carolyn Rosé
Wednesday, February 27 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, February 28, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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This talk explores the problem that workforce training is of increasing worldwide concern and yet has proportionately small (but growing!) representation within the learning sciences. Many well-meaning companies offer training opportunities to their employees, but when push comes to shove, companies feel pressure to push for short-term productivity over learning even if it may end in higher productivity in the long term. One issue is that in the absence of a clear path towards learning that does not compete with productivity, the reality of what it takes to survive as a company exerts pressures that oppose investment of employee time in learning. We believe the answer is to embed learning opportunities into work. Building on over a decade of AI-enabled collaborative learning experiences in the classroom and online, we are working within a new industry practice of Mob programming to create a paradigm for shared cognition in software development so that it will be possible to adjust the priorities between learning and productivity at different times. In this way, it is possible to offer learning opportunities integrated with work. A longer-term focus is quantifying the tradeoffs between learning and productivity to enable more reasoned decision making about prioritization over time, with the goal of offering a forecasting that enables shifting from a short-term optimization paradigm to a more long-term optimization. This talk reports on work that is currently situated within a multi-national online university course using the industry standard software development platform AWS Cloud9 as well as efforts to begin transitioning to industry.

#14 Towards the Present of Artificial Intelligence in Education
Jack Mostow, Ph.D.
Wednesday, March 6, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, March 7, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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This Webinar Series is entitled “the Future of AI in Education and Training.” Previous talks have described some of the emerging marvels made possible by advanced technologies when informed by cognitive, motivational, social, linguistic, and other aspects of human learning. But they typically rely on resources taken for granted in developed countries, including reliable electricity, fast WiFi, high-bandwidth web access, powerful computers, sophisticated sensors, and expert technical support to keep it all working. These resources are scarce or absent in most of the world. Nevertheless, artificial intelligence has tremendous potential to improve education in developing countries because even rudimentary capabilities could make such an enormous difference in the lives of children who have little or no access to effective schools. So although this series is about the future of AI in education, this talk is about its “present,” in both senses – its current state, and the tremendous gift it could offer: a world-class education for every child on the planet. The talk will present some predictions and prescriptions based on the presenter’s decades of experience with Project LISTEN’s Reading Tutor and its successor RoboTutor, a $1M Finalist in the Global Learning XPRIZE competition to develop an open-source tablet app that teaches basic literacy and numeracy.

#15 Machine Learning and Human Intelligence: The Future of Education in the 21st Century
Rose Luckin, Ph.D.
Wednesday, March 20, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Monday, March 25, 5:00 PM-6:00 PM Eastern / 2:00 PM-3:00 PM Pacific
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The development and application of Artificial Intelligence (AI) is bringing imminent and rapid change to almost every aspect of life, with today’s children experiencing a very different life to that of their parents. To prepare people for the anticipated changes to their lives, we must ensure that our education and training is tuned to the new demands of the workplace and society. The landscape we must navigate is likely to be bumpy and there will be significant challenges, not least, those relating to ethics. Fundamental to success will be unpacking our relationship with the concept of Intelligence. In thinking about how AI will impact on education and what sorts of knowledge and skills future citizens will need, we therefore need to look beyond the current trends towards trying to identify the jobs and skills that the world will require to the core issue of what it means to be intelligent in an AI augmented world. However, there is value in synthesizing what experts have investigated with respect to how susceptible jobs are to computerization because this is an important element of the context within which we need to re-conceptualise human intelligence.

#16 How Shared Scientific Instruments Can Revolutionize the Learning Sciences
Neil T. Heffernan
Wednesday, April 10, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, April 11, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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Shared scientific instruments help address big challenges in research. The Hubble Space Telescope has helped hundreds of astronomers do studies that none of them could have done if they had to also build the instrument they use. Physicists benefit from shared particle accelerators that are expensive to build and they test their own ideas to better our understanding of the universe. Shared scientific instruments make science more efficient since no single scientist need create and validate the instruments. In this webinar, I will discuss the importance for the shared research infrastructure in the social sciences, particularly the learning sciences, to support open research. I believe nonprofit, university-based platforms can make great strides in the learning science fields by creating shared scientific instruments.

#17 The Magnificent Seven: Seven Roles That AI Can Play to Transform Learning
Dr. W. Lewis Johnson
Guest host: Kurt VanLehn
Wednesday, May 1, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, May 2, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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At the start of this webinar series, we asked whether AI replace teachers in education and training. This webinar synthesizes what we have learned over the course of the series and draw conclusions about the roles that AI can play in improving learning teaching. The goal should be not to replace teachers but to work alongside teachers, to assist them in their work and perform tasks that are time consuming and difficult for teachers to perform on their own. We can identify at least seven roles that AI can play in the learning and teaching process: Communicate, Assess, Critique, Guide, Analyze, Orchestrate, and Construct. The availability of learner data increasingly makes all of these roles possible. As AI combines these roles, it sets the stage for a fundamental transformation of learning processes and learning organizations.

#18 Artificial Intelligence Applications to Support K–12 Teachers and Teaching
Robert F. Murphy, Ph.D.
Senior Policy Researcher, RAND Corporation
Wednesday, September 4, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, September 5, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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Recent applications of artificial intelligence (AI) have been successful in performing complex tasks in health care, financial markets, manufacturing, and transportation logistics, but the influence of AI applications in the education sphere has been limited. However, that may be changing. In this Webinar, I will discuss several ways that new AI-fueled technical capabilities can be used to support the work of K–12 teachers by augmenting teacher capacity rather than replacing teachers. I will highlight promising applications that are currently being deployed in schools and classrooms as well as future applications on the horizon. My presentation will conclude with a discussion of some of the key technical and policy challenges that need to be addressed in order to realize the full potential of AI applications for educational purposes.

#19 Challenges for the Future of Artificial Intelligence in Education
Ryan Baker
Associate Professor, University of Pennsylvania and Director of the Penn Center for Learning Analytics
Wednesday, October 16, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
Thursday, October 17, 1:00 PM-2:00 PM Eastern / 10:00 AM-11:00 AM Pacific
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As the talks in this seminar series have shown, artificial intelligence has had a positive impact on education. After only a brief number of years of research, we have accurate models of constructs many didn’t think we could model, dashboards and interventions and (some) evidence they work, and scaled solutions that are being used to change student outcomes. As a field, we have solved some challenging problems. So, what’s next? In this talk, I’ll discuss a few hard problems that could block AI in education from reaching its full potential; some of the big goals I think we can strive to achieve; some of the grand challenges we will need to — and I think can — solve; and perhaps most importantly — how we’ll know if we’ve gotten there.


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).
Art Graesser, Ph.D.
Professor, Department of Psychology and the Institute of Intelligent Systems at the University of Memphis

Art Graesser is a professor in the Department of Psychology and the Institute of Intelligent Systems at the University of Memphis, as well as an Honorary Research Fellow at the University of Oxford. He received his Ph.D. in psychology from the University of California at San Diego. His research interests question asking and answering, tutoring, text comprehension, inference generation, conversation, reading, problem-solving, memory, emotions, artificial intelligence, computational linguistics, and human-computer interaction. He served as editor of the journal Discourse Processes and Journal of Educational Psychology, as well as presidents of 4 societies, including Society for Text and Discourse, the International Society for Artificial Intelligence in Education, and the Federation of Associations in the Behavioral and Brain Sciences.
Carolyn Rosé
Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University

Her research focuses on understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. She serves as Past President and Inaugural Fellow of the International Society of the Learning Sciences, Senior member of IEEE, Founding Chair of the International Alliance to Advance Learning in the Digital Era, Executive Editor of the International Journal of Computer-Supported Collaborative Learning, and Associate Editor of the IEEE Transactions on Learning Technologies.
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.
Jack Mostow, Ph.D.
Emeritus Research Professor of Robotics, Machine Learning, Language Technologies, and Human-Computer Interaction at Carnegie Mellon University

Jack Mostow leads the RoboTutor team, a $1M Finalist in the $15M Global Learning XPRIZE competition to develop an open-source tablet app that teaches basic literacy and numeracy to children in developing countries. He previously founded Project LISTEN to develop an automated Reading Tutor that listens to children read aloud. It won AAAI94’s Outstanding Paper Award, a U.S. patent, inclusion in NSF’s Nifty Fifty, and the Allen Newell Medal of Research Excellence. Prof. Mostow earned his A.B. cum laude in Applied Mathematics from Harvard University and his Ph.D. in Computer Science from Carnegie Mellon University.
Neil T. Heffernan
Director of the PhD Program in Learning Science and Technologies, Worcester Polytechnic Institute.

Neil T. Heffernan enjoys doing educational data mining and running the ASSISTments system. ASSISTments helps schools teach better. It’s a web service hosted at WPI that allows teachers to assign nightly homework or daily class work. Students get instant feedback while teachers get live reports. Professor Heffernan enjoys supervising WPI students in creating ASSISTments content and features. He has 6 dozens paper in educational data mining, and 20+ papers in comparing different ways to optimize student learning.
Robert F. Murphy, Ph.D.
Senior Policy Researcher, RAND Corporation

Robert Murphy is a senior policy researcher at the RAND Corporation. Murphy’s research focuses on research and evaluation of innovative educational and workforce training programs and technologies. His research combines studies of program implementation and impacts on program participants using qualitative methods and experimental and rigorous quasi-experimental designs. Murphy has designed and led a wide range of research projects on the use and impacts of instructional technologies across a variety of learning settings (K–12, higher education, and adult basic education) for a variety of government agencies, foundations, and commercial clients. Among his current projects, Murphy is the co-principal investigator of a study of the efficacy of an online homework support platform for mathematics and two separate randomized control trial evaluations of new science curricula aligned to the Next Generation Science Standards and published by Amplify Science.
Ryan Baker
Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics

Baker’s lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 160 researchers in 6 countries. Predictive analytics models he helped develop have been used to benefit hundreds of thousands of students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade.