Personalization of learning content can have a positive impact on learning in a computer based environment. Personalization can occur in a number of different ways, such as including an individual's name or entered content throughout the learning materials, or selecting examples based on self-reported preferences. The Generalized Intelligent Framework for Tutoring (GIFT) is an open-source, domain independent intelligent tutoring system framework. GIFT includes a number of different authoring tools (e.g., GIFT Authoring Tool, Survey Authoring System) that can be used to generate adaptive courses. In its current form, GIFT does not have specific mechanisms to support personalization of materials to the individual user based on pre-entered preferences. The current paper describes ways that personalization research has previously been conducted with GIFT. The paper additionally provides recommendations on new features that could be added to GIFT'S authoring tools in order to support personalizing learning materials, guidance, and surveys that are provided to the learner.
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