The Generalized Intelligent Framework for Tutoring (GIFT) is an open source framework for creating Intelligent Tutoring Systems (ITSs). GIFT can provide tailored instruction and remediation that takes into account the current state of the learner, and learner attributes such as individual differences in various domains (Sottilare, Brawner, Goldberg, & Holden, 2012; Sottilare, Brawner, Sinatra, & Johnston, 2017). GIFT is available in both downloadable and in online form (known as GIFT Cloud at https://cloud.gifttutoring.org). GIFT includes authoring tools that can be used to create “GIFT courses,” which are a sequence of materials, questions, and instruction that is presented to a learner. While GIFT is primarily a system for authoring ITSs, it can also be leveraged for use in experimentation in both traditional and ITS relevant experiments. For the purposes of experimentation, one of the major advantages of GIFT is its ability to extract participant data from GIFT courses through the use of either the desktop based Event Report Tool (ERT) or the GIFT Cloud Event Report Tool (Cloud ERT). Each time learners participate in a GIFT course, a log file is created that includes all of their entered data, responses to questions, and a record of their actions. Using the Event Report Tools, experimenters can select the specific GIFT data pieces of interest and export those as comma separated value files, which can be easily imported into Microsoft Excel. The Army has expressed a growing need for applying ITS approaches to teams, through Intelligent Team Tutoring Systems (ITTSs). There is also an increase in interest in developing GIFT Cloud to provide a proper mechanism for collecting team-based data. Part of creating a framework for ITTSs is not only providing guidance and authoring tools for the collection of team performance data, but also export tools that provide data in an understandable way. While both the team authoring and export aspects of GIFT are not currently implemented, this chapter’s focus provides a starting point on how to make the export tools (ERT) more suitable for team-based data collection. The current chapter will focus on the team elements, while also providing recommendations for overall improvements to the ERT’s flow and organization. Although the emphasis is on teams, the suggestions provided can help individual-based data collection as well.