  Generative Artificial Intelligence (GAI) can be seen as a double-edged weapon
in education. Indeed, it may provide personalized, interactive and empowering
pedagogical sequences that could favor students' intrinsic motivation, active
engagement and help them have more control over their learning. But at the same
time, other GAI properties such as the lack of uncertainty signalling even in
cases of failure (particularly with Large Language Models (LLMs)) could lead to
opposite effects, e.g. over-estimation of one's own competencies, passiveness,
loss of curious and critical-thinking sense, etc.
  These negative effects are due in particular to the lack of a pedagogical
stance in these models' behaviors. Indeed, as opposed to standard pedagogical
activities, GAI systems are often designed to answers users' inquiries easily
and conveniently, without asking them to make an effort, and without focusing
on their learning process and/or outcomes.
  This article starts by outlining some of these opportunities and challenges
surrounding the use of GAI in education, with a focus on the effects on
students' active learning strategies and related metacognitive skills. Then, we
present a framework for introducing pedagogical transparency in GAI-based
educational applications. This framework presents 1) training methods to
include pedagogical principles in the models, 2) methods to ensure controlled
and pedagogically-relevant interactions when designing activities with GAI and
3) educational methods enabling students to acquire the relevant skills to
properly benefit from the use of GAI in their learning activities
(meta-cognitive skills, GAI litteracy).
