Serendipitous discovery, invention or artistic creation are among the most exciting and utmost relevant phenomena strongly related to human learning. At the moment, there are very few measurable criteria helping to understand and foster serendipity. In other papers [1–3] we have presented, discussed and exemplified a new paradigm/model/method/system/environment – called ViewpointS – that represents our efforts to overcome many current existing limitations in generic Information Systems or search engines (e.g.: Google) as well as in other social media (e.g.: recommender systems) offering information retrieval solutions based on the proximity of available resources.We also have also exposed howViewpointS may facilitate serendipitous discovery in an unprecedented way. In this paper, we wish to further motivate this last conjecture by proposing to explore two main research directions that did not convey sufficient attention by previous researchers (in particular those active in recommender systems): 1. assessing brain states in order to understand and forecast serendipitous human learning events triggered by emotions; 2. enhancing collective wisdom, since Human-Computer Interactions do not occur today between a human and a single machine (or algorithm), but within a community of humans and machines that continuously update “knowledge” beyond the scene. Both directions (assessment of brain states, collective wisdom) are currently on separate ways; we propose to combine them within one unified approach called ViewpointS.