Workshop on Les Contes du Mariage : Should AI stay married to Ed ?

This position paper explores the relationship between the historic roots of AIED and the challenges of restricting our vision to EdTech that has AI. It argues that the founders of AIED had a broad vision of the field, primarily driven by the goals of creating advanced technology for personalised learning. They were not wedded to a techno-centric view, demanding use of particular techniques that are now thought of as “AI”. The paper argues that we have accepted work with no AI, notably in Open Learner Modelling. We discourage, either directly or just because of our name, work that is true to the AIED founders’ vision. In doing so, we miss many exciting and promising ways to create better technology for education. 1 What was the AI in the initial vision of AIED? So how did we come to be called AIED in the first place? In the early days of computing research, AI had a very broad brief. It was driven by the vision that computers would one day be able to emulate the actions we describe as intelligent when people do them. What a bold vision this was --at a time when computers were very slow, expensive and available only in research labs, military and business contexts. AI research stood in stark contrast to other the major areas of computing, such as hardware, operating systems, programming languages and numerical analysis. It was AI that looked to real world applications and creating the visions of science fiction. AIED was born in the 1970s, with its first conferences in the 1980s (Self, 2015). It aspired to create applications that could help people learn. This was long before it was possible for most learners to even see, let alone use, a computer. A widely cited driver for our AIED research was the vision that computers could help achieve Bloom’s famous 2-sigma learning benefits from personalized teaching by an expert teacher (Bloom, 1984). Our community is still committed to this goal. But it is useful to consider what it meant. AIED 2015 Workshop Proceedings Vol 4 1 The classic early work in AIED identified four key elements: • domain expertise; • teaching expertise; • student model; and • user interface. And so, the goal of researchers was to explore any or all of these architectural elements, towards building what was called an Intelligent Tutoring Systems (ITS) or AIED system. Overall, for both AIED and ITS, one key goal was to create computer systems that could provide personalised teaching, just as a knowledgeable teacher with expert teaching skills could do. We still aim to do this. And another goal was to support excellent user interfaces --with what we may now call natural user interfaces (such as natural language and speech) and rich forms of interaction (such as graphical user interfaces that are now the norm). The spirit of their vision included creating systems and interfaces that both mimic human expert teaching and to use other techniques that are better suited to machines. Since our early days, when the AIED community chose its name, a great deal has changed for AI, computing broadly, even for the behemoth of formal education and the commercial interests associated with those institutions and broader education. In parallel, AIED research has evolved in important ways. The next part of this paper explores these differences as a foundation for arguing that AI still has a place in AIED, but that it is not necessary for the still worthy and, as yet, unreached core vision of our founders. 2 How has AI changed since the birth and naming of AIED? AI has become mainstream in the sense that it is part of the technology that each of us uses each day. This is well illustrated in the following descriptions from the EdX Introduction to AI. Artificial intelligence is already all around you, from web search to video games. AI methods plan your driving directions, filter your spam, and focus your cameras on 1 https://www.edx. org/course/artificial-intelligence-uc-berkeleyx-cs188-1x0#. VQzBWUaI0k4 AIED 2015 Workshop Proceedings Vol 4 2 faces. AI lets you guide your phone with your voice and read foreign newspapers in English. Beyond today's applications, AI is at the core of many new technologies that will shape our future. From self-driving cars to household robots, advancements in AI help transform science fiction into real systems. I have added the bold font to highlight the sampler of technical areas alluded to: planning, filtering, vision, natural language translation. AI has been so successful that it has resulted in many off-the-shelf tools for these tasks, and for many other core AI tasks. AI has also changed from its focus on deep reasoning to large-scale statistical methods. This partly reflects the huge drop in the cost of memory and processing, along with the availability of networking. So, for example, an area like natural language translation has shifted from an early focus on user modeling and deep reasoning to statistical techniques for machine learning that makes use of large corpus data, particularly text which occurs naturally in online materials such as books, newspapers, social media sites, Wikipedia. Where early work often involved complex reasoning, now it is possible, and sensible, to explore far simpler methods that harness huge amounts of data to achieve more robust and practical systems. AI has earned a place as part of a standard computing undergraduate degree. Similarly, some other core areas of the computing syllabus include databases, HCI, software engineering, graphics. Such areas have now established a substantial collection of techniques that belong in the computing professional’s toolkit. All of these, not just AI methods, should be used to achieve the core goals of AIED. AI has achieved much in its long history, often resulting in new communities that are more problem-, rather than technique-focused. For example, robotics researchers have their own publication venues; while they may also publish in AI venues when they create a new contribution to the body of knowledge in AI, their core goals are to create effective robots. High impact research may be based on new ways to make effective use of existing software tools for AI, database, graphical, language, vision systems. Similarly, separate communities have emerged in areas that are central to the AIED vision of effective interfaces, notably natural language generation and understanding and systems based on vision and depth sensing to provide NUI, natural user interaction. This offers support for learning away from the desktop. It AIED 2015 Workshop Proceedings Vol 4 3 opens possibilities for just-in-time learning, teachable moments and kinesthetic interaction that can be valuable for learning. In summary, AI is pervasive and it is just one, of many, software tools that AIED researchers should draw upon to create the future of technology to enhance learning and education. 3 How has education changed since the birth and naming of AIED? Over the history of AIED, computing has changed radically. Every potential learner in the developed world now has easy access to many forms of computers in their daily lives. And they will have many more, including personal devices, wearables, mobiles, portables and desktops and well as embedded systems such as interactive tables and walls and smart environments. The interface will have input modalities that include natural language, speech, gaze and gestures as well as keyboard and mouse. Diverse sensors will provide indirect input, such as eyetracking, mood detectors and activity trackers. Even in the developing world, there is increasing availability of personal technology, particularly mobile phones. This explosion of computing devices has finally begun to have a deep impact on education, both formal and informal. Our educational institutions make extensive use of computers. Those uses range from core productivity tools, through to tools for particular disciplines as well as personalized and collaborative learning tools. They link the formal and informal, for life-wide learning support. This has seen the emergence of communities that follow the AIED founder vision for using technology to enhance education. One recent example has seen the emergence of the Learning Analytics community. They represent the mainstream of education exploring ways to harness data from even administrative tools (such as those used to capture details of student demographics) and certainly for widespread learning tools, such as Learner Management Systems. AIED 2015 Workshop Proceedings Vol 4 4 Another emerging example, this time for lifelong, life-wide learning is due to sensor technology. For example, wearable activity trackers can be viewed as a valuable data source to an AIED system. They are a form of the interface element, just as surely as a keyboard, drawing tablet or spoken input is. Such sensors can play a key role for personalized teaching, such as interfaces to help people set effective goals and plans, self-monitor progress on these, discover which personal strategies are effective for achieving goals and to learn about new strategies. Yet another recent EdTech innovation is the MOOC. This is exciting on several levels. MOOCs offer the possibility for a very broad population of learners to have access to high quality personalised learning opportunities. MOOC platforms emerged from the elite computer science research world. This is striking as computer scientists, with outstanding expertise in diverse areas of computing, have so clearly committed to creating innovative teaching systems. MOOCs provide exciting green fields for EDM and for translating our years of AIED research into widely used software systems. These illustrate just three of many trends that matter for AIED. They are pervasive and have high impact. All are currently outside the core of what some members of our community see as AIED. There is a real risk that a paper reporting any of these would be rejected for lacking AI. And authors may