We describe the in-class evaluation of two versions of a tutorial dialogue system with 338 volunteers from a first-year undergraduate health-sciences class. One version uses supervised machine-learning techniques to classify student free-text responses; the other requires students to select their preferred response from a series of options (menu-based). Our results indicate that both the free-text and menu-based tutors produced significant gains on immediate post-test scores compared to a

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... l group. In addition, there was no significant difference in performance between students in the free-text and menu-based conditions. We note specific analysis work still to do as part of this research and speculate briefly on the potential for using tutorial dialogue systems in real class settings.