ChatGPT is a fascinating AI text generator tool. It is a language model developed by OpenAI, a research and deployment company with the mission, according to OpenAI’s website: “to ensure that artificial general intelligence benefits all of humanity”. ChatGPT is able to generate human-like texts. But how does it work? What about the quality of the texts it provides? And is it capable of being self-reflective? Information sources must be efficient, effective and reliable in education, in order to enhance students’ learning process. For this reason, we started a dialogue with ChatGPT-3 while using, among others, a SWOT analysis it generated about its own functioning in an educational setting. This enabled us, as human authors, to analyze the extent to which this AI system is able to practice self-reflection. Finally, the paper sketches implications for education and future research.

We present a high-resolution isotope stratigraphy based on Globigerinoides ruber (white) over the past 2500 years in the Gulf of Taranto, central Mediterranean. G. ruber (white) reflects summer conditions in the Gulf of Taranto but is influenced by two major surface water masses: the Western Adriatic Current (WAC) and the Ionian Surface Water (ISW) and their variations on a decadal to multicentennial scale. Our analysis of the δ¹³C and δ¹⁸O of G. ruber (white) allows the distinction of several climatic periods: the ‘Roman Warm Period’ (RWP) (450–0 bc), with relatively wet and warm conditions and a higher influence of the WAC; the ‘Roman Classical Period’ (RCP) (ad 1–200) characterized by salinity increase resulting from circulation changes; the ‘Dark Ages Cold Period’ (DCP) (ad 500–750), where wetter conditions in the Gulf of Taranto region are coherent with an increase dominance of the WAC; the ‘Medieval Warm Period’ (MWP), with wet and warm conditions in the first, and a gradual drying in the second half; and finally, the transition from the MWP to the ‘Little Ice Age’ (LIA), which is characterized by continuing dry conditions.