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Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, a user model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related to privacy, invisibility of personalization, errors in user models, wasted user models, and the broad issue of enabling people to control their user models and associated personalization. We offer scrutable user models as a foundation for tackling these problems.This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.

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Abstract.A core element of an adaptive hypertext systems is the user model. This paper describes Personis, a user model server. We describe the architecture, design and implementation. We also describe the way that it is intended to operate in conjunction with the rest of an adaptive hypertext system. A distinctive aspect of the Personis user model server follows from our concern for making adaptive systems scrutable: these enable users to see the details of the information held about them, the processes used to gather it and the way that it is used to personalise an adaptive hypertext. We describe how the architecture supports this. The paper describes our evaluations of the current server. These indicate that the approach and implementation provide a workable server for small to medium sized user collections of information needed to adapt the hypertext.

As technology has become ubiquitous in learning contexts, there has been an explosion in the amount of learning data. This creates opportunities to draw on the decades of learner modelling research from Artificial Intelligence in Education and more recent research on Personal Informatics. We use these bodies of research to introduce a conceptual model for a Personal User Model for Life‐long, Life‐wide Learners (PUMLs). We use this to define a core set of system competency questions. A successful PUML and its interface must enable a learner to answer these by scrutinising their PUML, aided by its scaffolding interfaces. We aim to give learners both control over their own learning data and the means to harness that data for the important metacognitive processes of self‐monitoring, reflection and planning. We conclude with a set of design guidelines for creating PUMLs. Our core contribution is a way to think about the design and evaluation of learning data and applications so that they give learner control and agency beyond simple data access and algorithmic transparency. What is already known about this topic There is decades of Artificial Intelligence in Education (AIED) research on learner modelling, personalisation and Open Learner Models (OLMs). There is a growing body of work on Personal Informatics. What this paper adds Drawing on the above research, we present a conceptual model showing how learning applications and data repositories relate to a Personal User Model for Life‐long, Life‐wide Learners (PUMLs). A set of competency questions to inform design and evaluation of PUMLs. Guidelines for designing interfaces that enable learners to scrutinise and control their learning data and models. Implications for practice and/or policy As universities create institutional repositories of learning data, our work takes a complementary, learner‐centred perspective of learning data, applications and repositories. PUMLs offer a mechanism to support student’s meta‐cognitive processes. PUMLs go beyond simplistic views of data access and transparency of algorithmic processes—empowering learners to scrutinise their long‐term data and its use.