The Occasion

“Next level?” The question mark is doing real work. The framing HfMT chose for the day was appropriately provisional: not a declaration that AI has already transformed music education, but an invitation to ask whether, in what direction, and at what cost.

The invitations that reach me for events like this tend to come with one of two framings. The first is enthusiasm: AI is coming, we need to get ahead of it, here are tools your students are already using. The second is anxiety: AI is coming, it threatens everything we do, we need to protect students from it. Both framings are understandable. Neither is adequate to the curriculum question, which is slower-moving and more structural than either suggests.

I prepared three sets of handouts. The first covered data protection — the least glamorous topic in AI education, and the one that most directly determines what can legally be deployed in a university setting. The second covered AI tools for students: what exists, what it does, and what critical thinking skills you need to use it without being used by it. The third covered AI for instructors: where it helps, where it flatters, and where it makes things worse.

This post does not recapitulate the handouts. It addresses the question I kept returning to across all three workshops: what does this change about what a music student needs to learn?

What the Technology Actually Is

My physics training left me professionally uncomfortable with hand-waving — including my own. Before discussing curriculum implications, it is worth being specific about what these tools are.

The dominant paradigm in current AI — responsible for ChatGPT, for Whisper, for Suno.AI, for Google Magenta, for the large language models whose outputs are now visible everywhere — is the transformer architecture (Vaswani et al., 2017). A transformer is a neural network that processes sequences by computing, for each element, a weighted attention over all other elements. The attention weights are learned from data. The result is a model that can capture long-range dependencies in sequences — text, audio, musical notes — without the recurrence that made earlier architectures difficult to train at scale.

What this means practically: these models are trained on very large corpora, they learn statistical regularities, and they generate outputs that are statistically consistent with their training distribution. They are not reasoning from first principles. They do not “know” music theory the way a student who has internalised harmonic function knows it. They have learned, from enormous quantities of text and audio, what tends to follow what. For many tasks this is sufficient. For tasks that require understanding of underlying structure, it is not — and the failure modes are characteristic rather than random.

BERT (Devlin et al., 2018) showed that pre-training on large corpora and fine-tuning on specific tasks produces models that outperform task-specific architectures on a wide range of benchmarks. The same transfer-learning paradigm has spread to audio (Whisper pre-trains on 680,000 hours of labelled audio), to music generation (Magenta’s transformer-based models produce melodically coherent sequences), and to multimodal domains. The technology is mature, improving, and available to students now. Knowing what it is — not just what it produces — is the starting point for any sensible curriculum discussion about it.

The Data Protection Constraint

Before any discussion of pedagogical benefit, there is a legal boundary that most AI-in-education discussions skip over. In Germany, and in the EU more broadly, the deployment of AI tools in a university setting is governed by the GDPR (DSGVO, Regulation 2016/679) and, at state level in NRW, by the DSG NRW. The constraints are not abstract: they determine which tools can be used for which purposes with which students.

The core principle is data minimisation: only data necessary for a specific, documented purpose may be collected or processed. When a student uses a commercial AI tool to get feedback on a composition exercise and enters text that could identify them or their institution, that data may be stored, processed, and used for model improvement by an operator whose servers are outside the EU. Whether such transfers remain legally valid under GDPR after the Schrems II ruling (Court of Justice of the EU, 2020) is contested — and “contested” is not a position in which an institution can comfortably require students to use a tool.

The practical upshot for curriculum design is this: AI tools running on EU servers with documented processing agreements can be integrated into formal coursework. Commercial tools whose terms specify US-based processing and model training on user data cannot be required of students. They can be discussed and demonstrated, but making them mandatory puts students in a position where they must choose between their privacy and their grade.

This is not a reason to avoid AI in teaching. It is a reason to be honest about the regulatory landscape, to distinguish clearly between tools you can require and tools you can recommend, and to make data protection literacy part of what students learn. The skill of reading a terms-of-service document and identifying the data flows it describes is not a legal skill — it is a general literacy skill that matters for every digital tool a music professional will use.

What Changes for Students

The question I was asked most often across the three workshops was some version of: “If AI can already do X, should students still learn X?”

The question is less simple than it appears, and the answer is not uniform across skills.

Skills where automation reduces the required production threshold do exist. A student who spends weeks mastering advanced music engraving tools for score production, when AI can generate a usable first draft from a much simpler description, has arguably spent time that could have been better allocated elsewhere. Not because the underlying skill is worthless — it is not — but because the threshold of competence required to produce a working output has dropped. The student’s time might be more valuable spent on something that has not been automated.

Skills where automation creates new requirements are more interesting. Transcription is a useful example. Automatic speech recognition — using models like Whisper for spoken-word transcription, or specialised models for audio-to-score music transcription — is now accurate enough to produce usable first drafts from audio. This does not eliminate the need for transcription skill in a music student. It changes it. A student who cannot evaluate the output of an automatic transcription — who cannot hear where the model has made characteristic errors, who does not have an internalised sense of what a correct transcription looks like — is unable to use the tool productively. The required skill has shifted from production to evaluation. This is not a lesser skill; it is a different one, and it is not automatically acquired alongside the ability to run the tool.

Skills that automation cannot replace are those that depend on embodied, situated, relational knowledge: stage presence, real-time improvisation, the subtle negotiation of musical meaning in ensemble, the pedagogical relationship between teacher and student. These are not beyond AI in principle. They are far beyond it in practice, and the gap is not closing as quickly as the generative AI discourse sometimes suggests.

The curriculum implication is not “teach less” or simply “teach differently.” It is: be explicit about which category each skill falls into, and design assessment accordingly. An assignment that asks students to produce something AI can produce is now testing something different from what it was testing two years ago — not necessarily nothing, but something different. The rubric should reflect that.

What Changes for Instructors

The same three-category analysis applies symmetrically to teaching.

Routine task automation is genuinely useful. Generating first drafts of worksheets, producing exercises at different difficulty levels, transcribing a recorded lesson for later analysis — these are tasks where AI can save meaningful time without compromising the pedagogical judgment required to make use of the output. Holmes et al. (2019) identify feedback generation as one of the clearer wins for AI in education: systems that provide immediate, targeted feedback at a scale that human instructors cannot match. A transcription model listening to a student practice and flagging rhythmic inconsistencies does not replace a teacher. It extends the feedback loop beyond the lesson hour.

Content generation with limits is where AI is most seductive and most dangerous. A model like ChatGPT can produce a reading list on any topic, a summary of any debate in the literature, a set of discussion questions for any text. The outputs are fluent, plausible, and frequently wrong in ways that are difficult to detect without domain expertise. Jobin et al. (2019) and Mittelstadt et al. (2016) both document the broader concern with AI opacity and accountability: when a model produces a confident-sounding claim, the burden of verification falls on the user. An instructor who outsources the construction of course materials to a model, and who lacks enough domain knowledge to catch the errors, is not saving time — they are transferring risk to their students.

Hallucinations — outputs that are plausible in form but false in content — are not bugs in the usual sense. They are a structural consequence of how generative models work. A model trained to predict likely next tokens will produce the most statistically plausible continuation, not the most accurate one. For music education, where historical facts, composer attributions, and music-theoretic claims need to be correct, this matters. The model’s fluency is not evidence of its accuracy.

Personalisation is the most-cited promise of AI in education (Luckin et al., 2016; Roll & Wylie, 2016) and the hardest to evaluate in practice. The argument is that AI can adapt instructional content to individual learners' needs in real time, producing one-to-one tutoring at scale. The evidence in formal educational settings is more mixed than the boosters suggest. What is clear is that personalisation at scale requires data — and extensive data about individual students’ learning trajectories raises the same data protection concerns already discussed, in more acute form.

The Music-Specific Question

I want to be direct about something that came up repeatedly across the day and that the general AI-in-education literature handles badly: music education is not generic.

The skills involved — listening, performing, interpreting, composing, improvising — have a phenomenological and embodied dimension that does not map cleanly onto the text-prediction paradigm that most current AI systems instantiate. Suno.AI can generate a stylistically convincing chord progression in the manner of a named composer. It cannot explain why the progression is convincing in the way a student who has internalised tonal function can explain it. Google Magenta can generate a continuation of a melodic fragment that is locally coherent. It cannot navigate the structural expectations of a sonata form with the intentionality that a performer brings to interpreting one.

This is not a criticism of these tools. It is a description of what they are. The curriculum implication is that music education must be clear about what it is teaching: the product — a score, a performance, a composition — or the process and understanding of which the product is evidence. Where assessment focuses on the product, AI creates an obvious challenge. Where it focuses on demonstrable process and understanding — including the ability to critically evaluate AI-generated outputs — it creates new opportunities.

The more interesting question is whether AI tools can make musical process more visible and discussable. A composition student who uses a generative model, notices that the output is harmonically correct but rhythmically inert, and can articulate why it is inert — and then revise it accordingly — has demonstrated more sophisticated musical understanding than a student who produces the same output without any generative assistance. The tool does not lower the standard; it shifts where the standard is applied.

There is an analogy in music theory pedagogy. The availability of notation software that can play back a student’s harmony exercise and flag parallel fifths changed what ear training and harmony teaching emphasise — but it did not make harmony teaching obsolete. It changed the floor (students can check mechanical correctness automatically) and raised the ceiling (more class time can be spent on voice-leading logic and expressive intention). AI tools are a larger version of the same displacement: the floor rises, the ceiling rises with it, and the pedagogical question is always what you are doing between the two.

Copyright and Academic Integrity

Two issues that crossed all three workshops and deserve direct treatment.

On copyright: the training data of generative music models includes copyrighted recordings and scores, the legal status of which is actively litigated in multiple jurisdictions. When Suno.AI generates a piece “in the style of” a named composer, it is drawing on patterns extracted from that composer’s work — work that is under copyright in the case of living or recently deceased composers. The output is not a direct copy, but neither is the relationship to the training data legally settled. Music students who use these tools in professional contexts should know that they are working in a legally uncertain space, and institutions should not pretend otherwise.

On academic integrity: the issue is not that students might use AI to cheat — they will, some of them, and they have always found ways to cheat with whatever tools were available. The issue is that current AI policies at many institutions are incoherent: prohibiting AI use in assessment while providing no clear guidance on what counts as AI use, and assigning tasks where AI assistance is undetectable and arguably appropriate. The more useful approach is to design tasks where AI assistance is either irrelevant (because the task requires live performance or real-time demonstration) or visible and assessed (because the task explicitly includes reflection on how AI was used and to what effect).

Three Things I Came Away With

After a full day of workshops, discussions, and the conversations that happen in the corridors between sessions, I left with three positions that feel more settled than they did in the morning.

First: the data protection question is not separable from the pedagogical question. Any serious curriculum discussion of AI in music education has to start with what can legally be deployed, not with what would be useful if constraints were not a factor. The constraints are a factor.

Second: the skill most urgently needed — in students and in instructors — is not AI literacy in the sense of knowing which tool to use for which task. It is the critical capacity to evaluate AI-generated outputs: to notice what is wrong, to understand why it is wrong, and to correct it. This requires domain expertise first. You cannot critically evaluate an AI-generated harmonic analysis if you do not understand harmonic analysis. The tools do not lower the bar for domain knowledge. They raise the bar for its critical application.

Third: the curriculum question is not “how do we accommodate AI?” It is “what are we actually trying to teach, and does the answer change when AI can produce the visible output of that process?” Answering that honestly, skill by skill, for a full music programme, is slow work. It cannot be done at a one-day event. But a one-day event, if it is well-designed, can start the conversation in the right place.

HfMT’s Thementag started it in the right place.

References

• Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://arxiv.org/abs/1810.04805

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org

• Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

• Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2

• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.

• Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679

• Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3

• Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762

Based on a manuscript with colleagues from the RAPP Lab. Not yet peer-reviewed.

The Gap Nobody Talks About

There is a version of the NMP success story that stops too early. It goes: we installed LoLa, measured the latency, it came in at 9.5 ms to Vienna, the musicians played together across 745 km, it worked. Success.

What this story skips is the classroom after the demo. The student who can follow a setup checklist perfectly and still has no idea what to do musically when the connection is stable. The ensemble that gets a clean signal running and then plays exactly the same repertoire in exactly the same way they would in a co-present rehearsal, fighting the latency instead of working with it, frustrated when it does not feel right. The assessment rubric that checks off “maintained stable connection” and “completed the performance” and has nothing to say about everything that actually constitutes musical learning in a networked context.

The gap between technical feasibility and educational transformation is the subject of this post. Closing it turns out to require a different kind of curriculum design than most conservatoires have tried.

What Gets Taught Versus What Needs to Be Learned

The default curricular response to NMP has been to treat it as a technical skill with an artistic application. Students learn to configure an audio interface, manage routing, establish a LoLa connection, and then — implicitly — go do music. The technical content gets staged as a prerequisite to the “real” work.

This ordering is wrong in a specific way. Technical setup work is genuinely necessary, but making it a prerequisite treats the relationship between technology and musical practice as sequential rather than recursive. In practice, the interesting musical problems only become visible through the technical ones. A student does not understand why buffer size matters until they have felt the difference between a 5 ms and a 40 ms offset in a coordination-intensive passage. A student does not develop an opinion about audio routing configurations until they have experienced a rehearsal collapse caused by a routing error they could have prevented.

The RAPP Lab’s recurring insight across several years of module iterations at HfMT Köln was more direct: once learners can establish a stable connection, the harder challenge is developing artistic, collaborative and reflective strategies for making music together apart. Technical fluency is a foundation, not a destination.

The Curriculum We Ended Up With

It took several cycles to get there. The early format was weekend workshops — open, exploratory, no formal assessment, primarily for advanced students who self-selected in. These were useful precisely because they were informal: they revealed quickly how technical and musical questions become inextricable once you are actually playing, and they gave us evidence about where students got stuck that we would not have found from a needs analysis.

Over time, elements of those workshops were developed into recurring curriculum-embedded modules, which then fed into independent study projects and eventually into external collaborations and performances. The trajectory mattered: moving from a one-off event to something longitudinal meant that knowledge built across cohorts rather than resetting every time.

The module structure that emerged has three interlocking elements:

Progressive task design. Early sessions are tightly scoped: specific technical-musical exercises, limited repertoire, well-defined success criteria. Later sessions move toward open-ended projects, student-led rehearsal planning, and eventually cross-institutional partnerships where variables are genuinely outside anyone’s control. The point of the early constraints is not to make things easier — it is to create conditions where students can notice what they are doing rather than just surviving.

Journals and debriefs. Students kept individual reflective journals throughout modules, documenting not just what happened but how they responded to it — technical problems, musical decisions, moments of coordination failure and recovery, questions they could not answer at the time. Group debriefs after each rehearsal then turned those individual threads into collective knowledge: comparing strategies, naming the problems that came up repeatedly, developing shared language for rehearsal coordination.

The debrief is the part of this model that I think gets undervalued. It is not just reflection — it is curriculum production. Strategies that emerged from one cohort’s debriefs became documented starting points for subsequent cohorts. Knowledge accumulated rather than evaporating when the semester ended.

Portfolio assessment. Rather than assessing primarily on a final performance, students assembled portfolios that could include curated journal excerpts, rehearsal documentation, reflective syntheses, and accounts of how their thinking changed. The question being assessed was not “did you play the concert” but “can you articulate why you made the decisions you made, and what you would do differently.”

What Students Actually Learn (When the Curriculum Works)

Four outcomes recurred across the RAPP Lab iterations, consistently enough to be worth naming:

1. Technical agency

This is different from technical competence. Competence means you can follow a procedure. Agency means you understand the procedure well enough to deviate from it intelligently when something goes wrong — to diagnose what failed, generate a hypothesis about why, and try something different.

The shift happened when students stopped treating technical problems as interruptions to the music and started treating them as information about the system they were working inside. A dropout is not just an annoyance; it is evidence about where the failure occurred. Getting to that reframe took, on average, several weeks of structured reflection. It did not happen from reading documentation.

2. Adaptive improvisation

Latency changes what real-time musical coordination can mean. You cannot rely on the same multimodal cues — breath, gesture, shared acoustics — that make co-present ensemble playing feel intuitive. You have to develop explicit cueing systems, turn-taking conventions, contingency plans for when the connection degrades mid-performance.

What we observed was that this constraint generated a specific kind of musical creativity. Students improvised not just with musical material but with rehearsal organisation itself — inventing systems, testing them, discarding the ones that did not work, documenting the ones that did. Some of the most musically interesting moments in the modules came from sessions where the technology was behaving badly and students had to make it work anyway.

There is research on “productive failure” — deliberately designing tasks that exceed students’ current control, because the struggle and recovery produces deeper learning than smooth execution (Kapur 2016). NMP turns out to be a natural context for this, not by design but because the network does not cooperate on schedule.

3. Collaborative communication

Co-present rehearsal relies heavily on implicit communication: the physical space makes many things legible without anyone having to say them. In a networked rehearsal, the spatial and gestural channel is degraded or absent. Students had to make explicit what would normally be implicit — articulating coordination strategies, naming the problems they were experiencing rather than hoping the ensemble would notice, developing a vocabulary for talking about timing and latency as musical parameters.

This turned out to generalize. Students who had worked through several networked rehearsal cycles were noticeably better at explicit musical communication in co-present settings too, because they had been forced to develop the vocabulary in a context where it was necessary.

4. Reflective identity

The students who got the most out of the modules were the ones who stopped waiting for the conditions to improve and started working with the conditions as they were. Latency as a compositional constraint rather than a defect to be routed around. Uncertainty as an artistic condition rather than a technical failure.

The journal entries where this shift is most visible are not the ones that describe what the student did. They are the ones that describe a change in how the student understands their own practice — who they are as a musician in relation to an environment they cannot fully control. That is a different kind of outcome than anything a timing metric captures.

The Assessment Problem

The hardest part of all of this to translate into institutional language is assessment. The conservatoire has well-developed frameworks for evaluating performances. It has much weaker frameworks for evaluating the learning that happens before and between and underneath performances.

Checklist rubrics — was the connection stable, was the latency within acceptable range, did the performance complete — are useful for safety and reliability. They are poor evidence for whether a student has developed the capacity to work reflectively and artistically in a mediated ensemble environment. A student who achieved a stable connection by following instructions exactly and a student who achieved it by diagnosing a routing error mid-session look identical on a checklist. They have had very different learning experiences.

Portfolio assessment addresses this by making the reasoning visible. When a student can explain why they chose a particular buffer configuration given the specific network characteristics of that session, how that choice affected the musical phrasing in the piece they were rehearsing, and what they would change next time — that is evidence of something real. It is also harder to assess than a timing log, which is probably why most programmes avoid it.

The argument is not that quantitative indicators are useless. It is that they function better as scaffolding for reflective judgement than as the primary evidence of learning. Mixed assessment ecologies — technical logs plus journals plus portfolio syntheses — are more honest about what is actually happening educationally.

What This Does Not Solve

The model described here depends on teaching staff who can facilitate reflective dialogue, curate knowledge across cohorts, and participate in iterative curriculum redesign. That is a specific professional competence that is not automatically present in a conservatoire staffed primarily by performing musicians. The training and support structures needed to develop it are an open question this paper does not fully answer.

The curriculum is also not portable as-is. The RAPP Lab model emerged in a specific institutional context — HfMT Köln, specific partner network, specific funding structure, specific cohort of students. The four outcomes and the general pedagogical logic may transfer; the specific formats will need adaptation. Any institution that tries to implement this without going through at least one cycle of their own iterative development is likely to end up with a checklist version of something that works only when it is a living process.

And the technology keeps moving. LoLa is a mature platform but the ecosystem around it — network configurations, operating system support, hardware lifecycles — changes faster than curriculum documentation. Building responsiveness into the curriculum itself, rather than treating it as a fixed syllabus, is the structural answer. Easier to recommend than to institutionalise.

References

Barrett, H. C. (2007). Researching electronic portfolios and learner engagement. Journal of Adolescent & Adult Literacy, 50(6), 436–449.

Borgdorff, H. (2012). The Conflict of the Faculties. Leiden University Press.

The Design-Based Research Collective (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.

Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457

Lave, J. & Wenger, E. (1991). Situated Learning. Cambridge University Press.

Sadler, D. R. (2009). Indeterminacy in the use of preset criteria for assessment and grading. Assessment & Evaluation in Higher Education, 34(2), 159–179. https://doi.org/10.1080/02602930801956059

Schön, D. A. (1983). The Reflective Practitioner. Basic Books.

Wenger, E. (1998). Communities of Practice. Cambridge University Press. https://doi.org/10.1017/CBO9780511803932

Changelog

• 2026-01-20: Updated the Sadler (2009) reference title to “Indeterminacy in the use of preset criteria for assessment and grading,” matching the journal article at this DOI. Updated the Kapur (2016) reference to the full published title: “Examining productive failure, productive success, unproductive failure, and unproductive success in learning.”

Where Students Start

Before students encounter the transit method, most of them have a clear mental model of how exoplanet detection works: you point a large telescope at a nearby star, and if there is a planet, you see it. “You could see them [the exoplanets] with a telescope/binoculars” and “You can see them with an extremely powerful telescope” are typical responses from year 8–9 students before they work through an actual detection unit.

This is not an unreasonable starting intuition. Telescopes see things far away. Planets are things far away. The inference seems to follow.

What it misses is the contrast ratio problem. A star is not just brighter than its planets — it is overwhelmingly, almost incomprehensibly brighter. In visible light, a star like the Sun outshines Jupiter by roughly a billion to one. Against that glare, the planet is functionally invisible. Direct imaging of exoplanets is possible in special circumstances — young planets far from their stars, imaged in infrared — but for the vast majority of exoplanets, it is not a viable detection method.

The unit described in this article takes that misconception as its entry point and builds from there.

The Direct Imaging Experiment

The first experiment in the unit is a hands-on demonstration of why direct imaging is difficult.

The setup: a student points their smartphone camera at a small light source (a switched-on torch). Directly next to the torch, barely a few centimetres away, is a pin with a coloured head — the “exoplanet”. On the phone’s display, the pinhead is invisible. The torch (star) drowns it out completely.

Students can then investigate what would need to change for the pinhead to become visible. The answer they discover: block the torch with a small disc held in front of the camera at the right distance. With the direct glare suppressed, the illuminated pinhead becomes visible in the image.

This is a coronagraph in miniature. The same principle is used in real direct-imaging instruments like SPHERE on the VLT or the coronagraph in the Nancy Grace Roman Space Telescope. Students discover, experimentally, the essential idea: to see an exoplanet directly, you need to suppress the star’s light without blocking the planet’s.

The experiment also motivates a natural follow-on question: under what conditions does direct imaging work at all? Students can vary the pinhead distance from the torch and its size, exploring qualitatively the conditions under which the “exoplanet” becomes detectable even with partial suppression. The answer — large planets, far from their host star — matches the real observational bias: most directly imaged exoplanets are large, young (still warm from formation), and in wide orbits.

The Transit Experiment

Once the limits of direct imaging are established, the unit introduces the transit method as the primary indirect technique. The pedagogical structure is deliberate: students have already understood that you cannot usually see exoplanets directly, which motivates the question of how else you might detect them.

The transit experiment uses a lamp as the star, a ball moved by hand (approximately periodically) around the lamp, and an Android smartphone running phyphox as the light sensor. When the ball crosses in front of the lamp from the sensor’s perspective, the measured illuminance dips. Students see a real light curve — not a simulation, not a graph from a database, but something they produced themselves from a physical measurement.

Two phyphox experiment files are provided for download (via QR code in the article and at astro-lab.app):

Basic experiment: records the raw illuminance data and displays the light curve. The focus is qualitative — what shape does the dip have? What determines the depth? What determines the period? Students can formulate the relationship between dip depth and planet-to-star size ratio as a qualitative rule (the larger the planet relative to the star, the deeper the dip) without necessarily working through the mathematics.

Extended experiment: adds real-time calculations of the transit depth $\Delta F$, the maximum illuminance $I_*$ and transit illuminance $I_\text{transit}$, the transit duration, and the orbital period. For students who are ready for it, this allows a quantitative derivation of the “planet” radius from the light curve — given a known lamp radius and the measured transit depth:

The extended experiment also invites critical engagement with the model: the radius derived from the analogy experiment will differ from the actual ball radius, because the distance ratios in the tabletop setup are not to scale. Making that discrepancy explicit — and asking students why it arises — is good science practice.

Limits of the Transit Method

A recurring theme in the unit is that every detection method has limits, and understanding those limits is part of understanding the method.

For the transit method, the fundamental limit is inclination. A transit is only observable if the planet’s orbital plane is aligned (nearly edge-on) relative to our line of sight. Most exoplanetary systems, viewed from Earth, will not be aligned in this way. The transit method is therefore a biased sample: it preferentially detects planets in edge-on orbits, and it misses most planets entirely.

Students can explore this experimentally: tilt the plane of the ball’s orbit away from edge-on and observe what happens to the light curve. The dip disappears. This connects naturally to a broader point about how astronomical surveys work: when we report “X% of stars have detectable planets”, we are reporting a fraction that has been corrected for this and other observational biases.

The article includes a differentiation note: the limits investigation works well as an open inquiry task, with students formulating and testing their own hypotheses about what orbital configurations produce detectable transits.

Exoplanets as a Curriculum Bridge

One point the article makes explicitly is that exoplanets are not just an astronomy topic but a context that connects to multiple items in the German physics curriculum for Sekundarstufe I. The cross-connections include:

• Optics: the seeing process (why does the star outshine the planet?), shadow formation, refraction in telescopes
• Mechanics: orbital period, Kepler’s laws at a qualitative level, the habitable zone as a consequence of stellar luminosity and distance
• Thermodynamics: planetary surface temperature, the greenhouse effect, albedo
• Pressure: atmospheric pressure, habitability (a connection developed more fully in the Mission to Mars experiment)

The motivating context — could this planet host life? — sustains student engagement across these topics in a way that treating them in isolation does not.

What Comes After

The transit method is a productive entry point, but the search for extraterrestrial life does not end with planet detection. The article closes by noting that the detected exoplanets need to be analysed for habitability — which depends on orbital radius (habitable zone), stellar temperature, planet radius (mass is not available from transit data alone), atmospheric composition, albedo, and greenhouse effect.

Many of these can be connected back to physics experiment contexts, and the astro-lab project has developed smartphone-based analogy experiments for several of them. Detailed information on these is at astro-lab.app.

For the full pedagogical sequence — from the original astro-lab student laboratory, through the COVID pivot to home experiments, to the return to school — see The Lab Goes Home. For the exomoon extension, which takes the transit experiment further into the question of moon-hosted life, see Can a Planet Have a Moon?.

References

Küpper, A., & Spicker, S. J. (2023). Fremde Welten — Die Suche nach Exoplaneten mit Analogieexperimenten thematisieren. Unterricht Physik, 34(194), 4–9.

Küpper, A., Spicker, S. J., & Schadschneider, A. (2022). Analogieexperimente zur Transitmethode für den Physik- und Astronomieunterricht in der Sekundarstufe I. Astronomie+Raumfahrt im Unterricht, 59(188), 46–50.

Spicker, S. J., & Küpper, A. (2024). Exoplanet hunting in the classroom: An easy-to-implement experiment based on video-aided light curve analysis with smartphones. The Physics Teacher, 62(3). https://doi.org/10.1119/5.0125305

MSB NRW (2019). Kernlehrplan für die Sekundarstufe I — Gymnasium in Nordrhein-Westfalen: Physik. Ministerium für Schule und Bildung NRW.

Changelog

• 2025-10-03: Updated the DOI for Spicker & Küpper (2024) to the correct 10.1119/5.0125305.